Incentives are mismatched among SREs, SecOps, and application developers. These mismatches create challenges around how and what information is shared across siloed teams. This asymmetrical information creates a moral hazard where one team can shift deployment risk to another team, with no accountability back to the originating team. Risk shifting results in unstable applications, inefficient infrastructure, security issues and poor customer experience. All of that impacts your company’s bottom line.

Closing the Information Gap
Observability is positioned as a way to fix the information gap between these disparate groups. Observable systems allow users to ask questions about their behavior in an open-ended way, unlike more rigid monitoring systems. SREs and SecOps teams are already reaping benefits from observability, but mismatched incentives limit its effectiveness. Meeting the observability expectations of IT leaders requires pervasive instrumentation across applications, infrastructure and third-party software. Delivering that level of instrumentation has remained out of reach due to incentive mismatches, as well as human and infrastructure costs.

The main incentive for developers is shipping code quickly at a reasonable level of quality, meaning a low bug count. This contrasts with SREs, who are incentivized around uptime, performance and efficiency, while the incentives for SecOps teams are around risk reduction and breach mitigation.

Since SREs and SecOps teams don’t have insight into what developers have changed, it’s easy for performance or security problems to get thrown over the fence to operations. A fresh code push might only include minor changes and be harmless to existing operations. Or, it could replace large chunks of logic across the entire codebase, including adding calls to external and third-party applications. To the intrepid SRE and SecOps teams, one change looks like any other. They don’t get a peek behind the curtain because of the mismatched incentives. DevOps wants to deploy quickly. Waiting on approval from other teams slows down deployments, so comprehensive reviews don’t happen. This doesn’t mean DevOps teams are intentionally trying to sabotage partner teams. They’re simply acting in their own interests based on their incentives.

The challenge is that one party, the developers, has more information than other parties. That information asymmetry is what creates unbalanced risk sharing. Coping with information asymmetry has led to all kinds of new collaborative models, starting with DevOps and evolving into DevSecOps and other permutations like Biz DevSecOps.

True collaboration has been hard to come by. Early DevOps efforts are often successful, but scaling beyond five to seven teams is difficult because teams lack the breadth of experience in IT operations or the SRE capacity to staff multiple product teams. The change velocity DevOps teams can achieve is often far greater than SREs and SecOps can absorb, making information asymmetry worse.

If teams can’t maintain high levels of collaboration and communication, another option must be developed. Observability practices, like collecting all events, metrics, traces and logs, allow SREs and SecOps teams to interrogate applications about their behavior without knowing which questions they want to ask ahead of time. However, observability only works if applications, and the infrastructure they rely on, are instrumented. This creates another problem: who does the instrumentation?

The expectation is DevOps teams embed instrumentation into their code as part of the development process. While that’s a nice idea, there are four reasons this falls short. First, the quality of instrumentation varies. Many log statements are terse and only understandable by the developer that wrote them. The message “In function xyx123!” isn’t helpful to an SRE digging into a performance problem that cropped up in the latest release.

Second, instrumentation libraries vary by implementation, giving inconsistent results across language bindings. Open Telemetry is trying to improve this, but its progress is slow and still requires developers to do more work that, if we’re honest, doesn’t benefit them; it benefits SREs and SecOps. Now we’re back to those pesky mismatched incentives and information asymmetry problems again.

The third problem with instrumentation is the volume of data. Each instrumented application can produce terabytes of data each day. When you have robust instrumentation, the amount of data can be overwhelming, and extremely costly to analyze and store.

Lastly, instrumentation is isolated to the code your team wrote. That represents a fraction of the code you rely on. Vendor-provided services and APIs remain a black box, limiting your observability into those components.

Resolving information asymmetry across teams requires two things:

Pervasive, pluggable instrumentation capabilities for all code that doesn’t require developer involvement, and
An observability pipeline to filter, redact and enrich data, then route it to your analytics platform of choice.
Pervasive Instrumentation
Operations teams need instrumentation without having to go back to developers and beg them to add it to existing code. They need to turn it on and off as needed, and they need readily consumable data. They also need every piece of data they can get, including packet payloads, insight into encrypted data and so on. This goes well beyond what’s possible with today’s instrumentation options.

AppScope, a newly released open source project, is a new take on instrumentation. AppScope interposes itself between application threads and system libraries, tracking things like file system access, network and HTTP activity, as well as CPU and process activity. It also provides payload data, and because it sits between the application and encryption libraries, it also gives access to users’ cleartext data. Because it works with any Linux binary, SREs and operations teams can instrument anything, even code they didn’t write.

Observability Pipeline
The challenge with pervasive instrumentation is dealing with all the data generated by applications. Network, file system, and other system data can easily swamp destination APM and log analytics platforms, driving up licensing and infrastructure costs. While instrumentation data is vital to rebalancing risk sharing in organizations, you need a way to manage that data intelligently to get value out of it. This is where the observability pipeline comes in.

An observability pipeline sits between the sources of observability data and its target destinations. By acting as a strategic control point, the pipeline gives users control over how data is formatted, filtered, enriched, and redacted before it is routed to one or more destinations.

Observability pipelines help SREs and operations teams deal with the flood of instrumentation data by routing low value data to low-cost storage, like S3, while higher value information lands in APM and log analytics tools. Later, if you decide you need the data stored in S3 to add more context to your analysis, you can replay it back through the pipeline and enrich your data set.

Shifting business priorities are accelerating the pace of technology deployment across organizations, and teams are pressured to deliver faster. This faster pace of delivery pushes deployment risk to operations and security teams that lack visibility into changes developers have made across complex distributed systems. This information asymmetry also increases risk to the business as applications become less predictable and reliable over time.

Traditional methods of resolving the information mismatch haven’t worked because incentives across teams aren’t aligned. Adopting pervasive instrumentation and observability practices will give SREs and operations teams critical visibility into rapidly changing application and infrastructure environments, without disrupting the developer experience and process.

Cloud identity access management company Okta Inc. today revamped its developer experience with features that include new integrations and documentation in a general release that’s free for up to 15,000 monthly active users.

The updates are part of Okta’s new Starter Developer Edition that will provide developers tools to embed the company’s Okta authentication, authorization and user management into enterprise cloud applications at scale.

The company announced the product revamps during the first day of Oktane21, the company’s three-day virtual developer conference.

“Okta’s vision is to enable everyone to safely use any technology,” said Diya Jolly, chief product officer at Okta. “Developers are foundational to bringing that vision to life, and it’s our goal to make every piece of the development process easier with Okta. Developers can ramp up at no cost with the Starter Developer Edition.”

Okta Identity Cloud plays an instrumental role in the secure deployment and use of applications in the cloud, especially when it comes to role- and permission-based management. Knowing who is accessing information, how it’s being accessed and controlling how it’s being accessed is critical for security.

“Our reimagined developer experience delivers tools that seamlessly work with developers’ toolchains across whatever hybrid, cloud, or multicloud environment they’re building on,” said Jolly. “If developers need to do something with identity, they can do it with Okta.”

With new application programming interfaces, developers will find their footing with Okta faster than before, and this can be done with a redesigned console for all Okta customers that the company says will help deliver a faster time to market and full application lifecycle support.

The company has provided what it calls “Auth For All,” secure applications support and further identity infrastructure across a multitude of popular languages so that developers can get running in their preferred code faster than ever before with enhanced documentation.

Sample applications are also included in the developer portal with highly detailed documentation including Go, Java, JavaScript, Python, VueJS, React Native and Spring Boot.

In addition, Okta has included integrations for DevOps, SecOps and API Security tooling. This means that developers can quickly prototype Okta’s identity capabilities across numerous use cases including continuous integration and delivery or roles and management when securing applications, APIs and infrastructure.

New integrations include Heroku to automate identity across CI/CD pipelines, Kong to protect APIs and an updated Okta Terraform provider, which can replicate Okta configurations across environments.

The new Okta Starter Developer Edition and integrations are generally available starting today on Okta’s developer portal page along with enhanced documentation, sample applications and implementations.

Web application security testing techniques have changed significantly since waterfall development methodologies lost popularity and the advantages of an agile approach to software development started to hold sway. Testing has had to become as iterative as the continuous development & integration methods of modern app development. In environments with multiple web properties, the automation of such testing is now a commonplace necessity.

Sure, manual testing still exists — and there are excellent arguments for engineers to pore through lines of code manually. However, the increasing need for security combines with pressure on DevOps teams to get applications into production quickly to produce situations for testers that are at worst untenable and, at best, potentially dangerous for the organization’s assets.

There is continuing friction between SecOps and DevOps, with developers feeling held back by security teams and security professionals feeling pressured to rein in developer activities to keep the larger enterprise infrastructure safe.

There have been many thousands of words written about this dichotomy in organizations that thrive and survive on getting web apps to market (many of those words appearing on this very site). Most of them urge businesses to bake best security practices into every stage of app development. That’s a fine aim, but many individuals and departments still feel under massive pressure to hit targets: the temptation is to either circumvent security to some degree (for DevOps) or prevent the rapid progress of applications to production (for SecOps).

Whatever the circumstances, there is always some kind of compromise across both fronts — unless of course web applications can be tested effectively, promptly, and without significant resource drain. Here is where the AST models come into play, typically comprising today of dynamic and static application security testing processes (DAST and SAST).

Both have advantages and disadvantages, mainly around the numbers of false positives raised by automated testing routines, the extra resource overhead required in terms of engineer hours or processing power, and the cost and effort required to deploy an effective solution.

Then there’s the problem of CI/CD: the testing processes have to be continuous at all stages of development, from initial concept and wireframing right through production and version updates.

Here, a new generation of interactive application security testing (IAST) platforms helps teams both hit DevOps targets and address the pressing concerns of the security team. Netsparker by Invicti combines DAST and IAST to provide automated security testing that overcomes the traditional shortcomings of standalone DAST or SAST tools. The combined approach results in applications being crawled 100%, verifiable proof that risks are real, and pinpointed references to the exact line of code or stack trace of the vulnerability — precisely the type of information that developers need to address issues quickly and effectively.

This approach means vulnerabilities are quickly found and reported complete with remediation guidelines and sample attack payloads that could be used by real-life attackers, so there is as little impediment to development progress as possible. Netsparker is the objective voice that shows and proves which issues constitute a real risk and that must be addressed immediately. Apps can continue through their development cycles, and true security concerns can be sent directly to be fixed as part of the developers’ daily workflows in the tools they already use.

The platform’s configurable multiple scanning agents act as distributed instances to maximize test coverage but also minimize resource utilization. Each scanning agent reports vulnerabilities centrally with concrete proof that the issue is real and exploitable. Objectivity from this canonical source promotes collaboration between developers and security teams, even in very complex web portfolios that are under constant development and testing. Interdependencies and shared libraries, hidden files, and application-level exploits are proactively probed, as well as cross-site scripting (XSS), SQL injection, and other vulnerabilities that account for the bulk of cybersecurity incidents.

It’s worth noting that the Netsparker platform doesn’t require a clean slate or a lengthy deployment process. Security and development engineers can continue to use their existing tool stacks, so investment in these remains valid and their lifespan is extended, promoting better ROI over time. Netsparker integrates with existing ticketing systems, Git* platforms, messaging and communication software, and even works alongside pen test tools like Metasploit.

It offers team management and vulnerability management features as a platform in its own right: it’s a central reference point for all security testing on just about any web application, old or new.

In a further article here on Tech HQ, we’ll be taking a more in-depth look at Netsparker’s combined DAST and IAST approach to application security. Web applications continue to be excellent ways for organizations to interact with partners, suppliers, customers, and internal stakeholders. The latest advances in interactive application security testing help to ensure that both legacy and new instances are kept as secure as possible in the face of emerging threats.

You can request a demo of Netsparker by Invicti to speak to a technical resource about your organization’s specific testing needs.

The move to continuous integration/continuous deployment (CI/CD) and agile development methodologies means that application development now requires network IT operation teams (NetOps) and security operation teams (SecOps) to work together, which puts increased stress on developers and engineers.

Add heterogeneous computing environments to the mix, and it’s no wonder that the role of application delivery and security within DevOps has never been more onerous. Selecting an application delivery solution that maximises automation and integrates into an existing DevOps environment is critical.

Here are four reasons why application delivery is critical to supporting DevOps.

Production-ready automation
CI/CD and agile development methodologies require IT experts from different domains, (i.e., application, storage, network security, application delivery) to deploy new application services and their associated policies. Traditionally, they’ve had to invest in long, tedious provisioning and programming tasks that are often repetitive and require specific domain expertise to execute.

In a DevOps-driven world, this is no longer possible. IT requires workflow automation so that even non-technical experts can develop and craft application delivery and security workflows. According to Radware research, over half (53%) of organisations don’t integrate application protection into CI/CD processes.

This is why IT operations require an evolution, transitioning from simple scripts to standard-based, production-ready modules. The goal? To enable anybody from different teams to deploy and manage services regardless of the domain expertise he or she possesses.

Automated ADC and security
The marketplace for application delivery and security solutions is as diverse as its capabilities. Selecting one that integrates with an application provisioning and deployment solution, such as Ansible, is critical to enabling end-to-end automation.

Traditionally, deploying a new application delivery service has meant writing and managing a monolithic, gargantuan script to automate the workflow. Each application deployment requires a different workflow, and thus, a different script. DevOps requires these monolithic codes to be broken into discrete operations to speed time to development and deployment.

Underlying application delivery and security services that support these applications are no exception to this rule. An enterprise-grade ADC should provide pre-defined/production-ready modules and integration with DevOps orchestration tools to allow non-technical users to spin up and deploy application and security services.

Traditionally, if an error occurs, a review and QA of the code would be required. In a module-based world, errors become more transparent and segmented. For example, if only the application security policy is incorrect, the problem can be identified, fixed and only the security policy rerun, versus the entire script-based workflow.

Reuse and repurpose
Production-ready modules provide the ability to reuse and repurpose. If a particular module, such as the application security module, requires an update, SecOps can make that update, create a subversion if necessary and apply it to other application workflows. Changes are isolated to the specific module and don’t impact other aspects of the workflow.

Finally, an application owner, versus an ADC expert, can make these changes to the application security module or other modules. For example, while application servers and security policies can reside with the application owner, VLAN operations might be a network administrator’s responsibility. Now, a single person can manage these usually disparate responsibilities, thereby saving time to market.

This modularised approach also means switching from staging to production and back seamlessly. Changes to workflow parameters made within a staging environment can be pushed to production following staging. These capabilities support CI/CD processes by minimising human errors and reducing downtime.

Enterprise-grade ADC capabilities
Finally, any application delivery solution must combine ease of use with advanced application delivery capabilities, such as optimising Layer 7 load balancing deploying and managing application security policies. This further enhances the capabilities of any DevOps orchestration tool.

Kubernetes is quickly consolidating its place as the leading container orchestration platform for cloud-native applications, with adoption at 59% among enterprise IT professionals as of March. But while Kubernetes delivers agility, flexibility and scalability for DevOps teams, it also creates complexity that can be an enigma for SecOps teams—especially when something goes wrong.

When it comes to detecting threats and tracking down breaches in Kubernetes, security teams’ key asset is the Kubernetes API server audit log. The audit log captures every successful or unsuccessful API server call, whether by human users, automation pipelines, controllers, operators, system components and, in general, API server operations performed by principals identified as service accounts. In other words, the audit log is the single source of truth for what’s happening inside a Kubernetes infrastructure.

But just because the audit log tracks all the activity in your Kubernetes environment doesn’t make it easy to find what you’re looking for. The larger your infrastructure, the more events there will be to parse through. The Kubernetes audit log is also extremely verbose, capturing a number of identifying attributes for each action, such as detailed information about what resource was accessed, who accessed it or what IP address the request came from. When the Kubernetes infrastructure at hand involves multiple clusters, which is the most common use pattern, it becomes a daunting task to place effective security monitoring controls across all clusters and manual security audit log monitoring becomes extremely impractical.

One simple approach to automate Kubernetes Audit log monitoring, which is quite useful for compliance purposes, is through the use of simple static policies that trigger alerts when a violation occurs. For example, an alert could be triggered if a human user accesses a workload that processes sensitive data. This simplified approach is useful when you have a precise notion, as in cases of regulatory compliance of what use patterns are allowed. However, in real-life deployments, adversaries never limit themselves to what is allowed.

Another approach is threshold-based security monitoring, which captures when API events, or combinations of events, rise above a certain threshold. However, this tactic can be easily bypassed by an adversary simply by controlling the rate of compromise attempts, spreading the attack out over time to avoid crossing the threshold.

Threshold-based based monitoring is also prone to false positives that can really wear out security and teams and create alert fatigue. For example, periodic batch processing workloads, which create a frenzy of API calls, would break through pre-configured thresholds but aren’t actually a threat. To separate threats from routine deployment events, you need to understand the context, something threshold-based monitoring alone cannot provide.

With significant, high-maintenance efforts, and potentially high rates of false alerts, threshold-based monitoring can help security or operations teams to catch known threats and vulnerabilities, but it won’t catch unknown threats or vulnerabilities. Again, to identify a threat, you need to be able to understand the context of any anomalous behavior: who is making the API calls, from where and what resources are they accessing. You also need to observe this data over time to establish what normal behavior looks like so you can differentiate it from anomalous behavior.

As a powerful, elastic and flexible cloud-native application infrastructure that drives many automatic processes, Kubernetes is inherently complex. To detect a security threat in Kubernetes, you need an adaptive security monitoring solution powered by machine learning. Through time-based observation of the various actors within a Kubernetes cluster that leverage the API server audit log, machine learning can learn and detect anomalous patterns in actor activity that simple tools will miss.

Learning the behavior of your clusters is no trivial task. Profiles for different users, components and automated services need to be built over time, and with so many moving parts, this is impractical to do manually. At the same time, new vulnerabilities are constantly coming to light, and there will always be vulnerabilities that are waiting to be uncovered. You can set alerts for vulnerabilities you know, but to catch unknown threats you need a strong baseline of activity in your cluster to monitor against, something that the right machine learning algorithms can readily capture and adapt to over time.

While machine learning is a broad topic, it is quite clear that the traditional approach of supervised or unsupervised offline model creations would not yield satisfying detection results, simply because different environments are accessed and behave differently. There has to be a component that learns and adapts to the specific Kubernetes environment being monitored.

Here are two security use cases that machine learning can reveal through the analysis of the Kubernetes audit log stream:

Stolen Service Account Tokens and User Credentials

Machine learning-based audit log monitoring tools can alert security teams of suspected credential theft. For example, if the same cluster credentials are reused from multiple different geographic or network locations within a short period of time, it can be flagged as an anomalous behavior and, if combined with additional abnormalities, could indicate stolen credentials.

By tracking and learning the geographical attributes and the access patterns of individual users or principals, machine learning can detect this anomalous behavior and flag it for security teams. In response, the security team can take actions to reduce or eliminate the blast radius—such as limiting access to the API server from the specific suspected IP address or modifying the Kubernetes role-based access control (RBAC) policy to reduce access privileges for the specific account.

Misconfigured Kubernetes RBAC

In addition to detecting threat actors that attempt to pivot within the Kubernetes cluster, machine learning can also leverage the audit log to detect attempted exploits of known or unknown vulnerabilities if the RBAC permissions are misconfigured or over-permissive. On the flip side, once RBAC policies are properly configured, reducing RBAC privileges to the required minimum, machine learning can alert when unauthorized users make unsuccessful API calls to access sensitive resources, indicating a failed exploit attempt.

Ultimately, the audit log is an incredibly rich source of information on activity within your Kubernetes environment and a valuable tool for both detecting threats and vulnerabilities and forensically tracing breaches. Although the complexity prohibits manual monitoring, machine learning can give security teams real-time observability into security incidents within their Kubernetes environments and enable informed investigations and audits that get to the root of problems more quickly.

Eficode Earns a Place in Gartner’s Agile and DevOps Hype Cycle

Eficode, a European DevOps services and platform company, was recognized in the Gartner Hype Cycle for Agile and DevOps, 2020. The report surveys key approaches and technologies that organizations use to underpin their DevOps initiatives, and inclusion signals that an analyst firm’s research community views a vendor as noteworthy within its category.

What Is the Gartner Hype Cycle?

Gartner Hype Cycles are a proprietary research methodology that plots the maturity, adoption, and business applicability of technologies across five phases: Innovation Trigger, Peak of Inflated Expectations, Trough of Disillusionment, Slope of Enlightenment, and Plateau of Productivity. For technology buyers, the model helps contextualize where a tool or practice sits in its real-world adoption lifecycle—useful when planning investment decisions and managing deployment risk.

Recognized as a Sample Vendor in DevOps Value Stream Management Platforms

Eficode was listed as a Sample Vendor in the DevOps Value Stream Management Platforms (VSMP) category. According to the report, VSMPs “provide visibility and traceability to every process in software delivery – from ideation through development to release and production, and extending to documenting feedback from customers.”

The report elaborated on the role VSMPs play in scaling DevOps, with Hassan Ennaciri, Senior Director & Analyst at Gartner, stating:

“To scale agile and DevOps practices, organizations need a DevOps VSMP that provides visibility into higher level metrics. DevOps VSMPs serve as an orchestrator by integrating with existing DevOps tools. They provide product teams with flexibility and freedom in selecting their tools by simplifying integration with existing DevOps toolchains or value stream delivery platforms (VSDPs).”

Hassan Ennaciri, Senior Director & Analyst, Gartner

What Is a DevOps Value Stream Management Platform?

A DevOps Value Stream Management Platform gives engineering and product leaders end-to-end visibility across the software delivery lifecycle. Rather than replacing existing tools, a VSMP integrates with them—CI/CD pipelines, issue trackers, source control, monitoring systems—and surfaces consolidated metrics such as lead time, deployment frequency, and change failure rate. This makes it possible to identify bottlenecks, enforce compliance gates, and tie engineering output to business outcomes.

Key capabilities typically associated with VSMPs include:

• End-to-end traceability: Linking a customer requirement to a commit, build, deployment, and production incident.
• Flow metrics: Measuring throughput, cycle time, and work-in-progress across the delivery pipeline.
• Toolchain integration: Connecting heterogeneous DevOps toolchains without forcing tool standardization.
• Compliance visibility: Providing audit trails required in regulated industries such as finance and healthcare.
• Feedback loops: Capturing production and customer feedback and routing it back to development teams.

Eficode ROOT DevOps Platform

The vehicle for Eficode’s recognition is its ROOT DevOps Platform, a managed platform designed to consolidate an organization’s DevOps toolchain. Ilari Nurmi, CEO at Eficode, commented on the recognition:

“We have built the Eficode ROOT DevOps Platform to help organizations manage their software delivery and DevOps toolchain. With Eficode ROOT, our customers deliver customer value faster, manage quality- and compliance-related risks, and improve the much-needed visibility into software development. We are thrilled that Gartner, the world’s leading research and advisory company, has recognized Eficode in their report. We view it as a great testament to the work we are doing.”

Ilari Nurmi, CEO, Eficode

Customer Use Cases Across Industries

At the time of the announcement, Eficode had worked with organizations across multiple verticals, demonstrating that DevOps toolchain management is an industry-agnostic challenge. Referenced customers included:

• Sennheiser – embedded software development
• DNA (now part of Telenor) – telecommunications
• OP – financial services

Around the same period, Eficode also announced an agreement with Bankdata, one of Denmark’s largest financial technology companies, to deliver the ROOT DevOps Platform. Bankdata selected Eficode for its ability to integrate best-of-breed individual solutions across software development and operations into a continuous value stream—a use case that directly illustrates the VSMP category Gartner described.

Why This Recognition Matters for DevOps Teams

Inclusion in a Gartner Hype Cycle as a Sample Vendor is not an endorsement, but it does signal that a vendor has reached sufficient market presence to appear on the radar of Gartner’s research process. For engineering leaders evaluating VSMP solutions, appearances in Hype Cycle reports can serve as a useful starting point for a shortlist—but should be supplemented with hands-on evaluation, reference checks, and proof-of-concept testing aligned to your specific toolchain.

The broader takeaway from the 2020 report is that the VSMP category itself was gaining traction as organizations moved beyond individual DevOps tool adoption toward systemic visibility across their entire delivery pipeline. That trend has only accelerated since, making the category increasingly relevant for enterprise DevOps practitioners today.

The Problem: Security Left Behind by DevOps Velocity

Rapidly maturing DevOps teams are breaking boundaries, strengthening processes, and shipping products at a faster pace with each iteration. For leadership, this means deadlines met and exceeded. For end users, it means faster, more reliable improvements to the apps and services they rely on every day. The adoption of DevOps practices is widely credited with delivering these efficiencies.

But ask a security professional about accelerating delivery cycles and the reception is considerably less optimistic. Research cited in the original reporting found that nearly 70% of security professionals say their CEOs will do almost anything to prevent development slowdowns — and 52% of those same professionals report that security has been sacrificed during development to meet a business deadline.

The downstream consequences are stark. Roughly 44% of developers are reportedly not equipped to write secure code, and approximately 42% of operations teams lack adequate security training. Data breaches are rising in both frequency and cost — with average breach costs in the US reported at around $7.9 million per incident.

Treating security as an afterthought — or worse, a bottleneck to be bypassed — is not a sustainable operating model. Financial penalties and remediation costs are significant, but the reputational damage caused by a preventable breach can be far harder to recover from.

The Good News: Security and DevOps Don’t Have to Be at Odds

Integrating security into existing DevOps workflows does not require abandoning agile principles or slowing down continuous deployment. The following three strategies help bridge the gap between security and DevOps teams in a practical, incremental way.

1. Shatter the Existing Perception of SecOps

Research shows that 85% of organisations consider building a stronger bridge between security and DevOps — often called SecOps — to be an important organisational goal. Yet nearly 20% of those same organisations report having no established SecOps practices whatsoever. Awareness without action is the defining failure mode here.

Changing this dynamic requires both sides to move. Security professionals need to communicate more effectively — framing their work in terms of business risk, not just compliance. Developers, engineers, and operations teams need to demonstrate a genuine willingness to listen and adapt.

A practical starting point is to schedule security work on a parallel track to development. Rather than having security reviews gate each release at the last moment, security teams work on their tasks asynchronously, building in proper controls without creating a bottleneck. This is also the foundation for a longer-term goal: integrating security testing and validation directly into the automated CI/CD pipeline.

When making the case to leadership, security professionals should emphasise a straightforward economic argument: identifying and fixing vulnerabilities early in the development lifecycle is dramatically cheaper and less disruptive than remediating them after deployment. Shift-left security is not just good practice — it protects the business.

2. Use Data to Make Informed Security Decisions

Not every security risk carries the same weight, and treating them uniformly is a recipe for either paralysis or neglect. Some vulnerabilities require immediate remediation; others can be deferred to the next sprint. Security teams need clear, documented thresholds that define when a flaw is severe enough to justify pushing back a release deadline — and those thresholds need to be agreed upon by engineering leadership and the business before a crisis forces the conversation.

Measurement is central to this. With each release, all security-relevant flaws and quality issues should be documented and compared against previous releases. The goal is continuous improvement, not accountability theatre. Over time, these metrics create a baseline that allows security teams to demonstrate progress quantitatively — making it far easier to resist pressure to deprioritise security when delivery deadlines loom.

Embedding security metrics into standard sprint reports and release reviews keeps security visible at the leadership level. When CEOs and product leaders see security data alongside velocity and quality data, security becomes part of the conversation rather than an afterthought.

3. Make Security Everyone’s Responsibility

In an ideal world, security is baked into a DevOps practice from day one. In practice, most organisations find that DevOps has built a mature, efficient delivery machine well before security has had a chance to integrate itself into the process. Catching up requires a deliberate strategy.

Security teams should actively cultivate champions in every department — developers, QA engineers, product managers, and project managers who understand the value of secure development practices and can advocate for them internally. Data and metrics, as described above, are the most persuasive tools available. Quantitative evidence of risk reduction is far more compelling to business stakeholders than abstract appeals to best practice.

Security professionals should also push to be included as early as possible in the development process — ideally from the requirements and design phase. Early involvement allows security guidance to be incorporated when it is cheapest to act on, rather than retrofitted at deployment time.

Finally, security awareness must extend beyond technical roles. Project managers and product owners control budgets and release timelines. If they do not understand or value security input, security teams will consistently lose the argument when it matters most. Regular communication, shared metrics, and early involvement in planning cycles are the most effective ways to change this dynamic.

The Path Forward: From Security Silo to Shared Ownership

By stating their case clearly, engaging early, and backing recommendations with data, security professionals can demonstrate that security is not a drag on the development process — it is a prerequisite for sustainable delivery. DevOps teams that invest in bridging the security gap will find that the integration pays dividends in reduced incident costs, stronger products, and greater organisational resilience.

The security silo is a choice, not an inevitability. Breaking it down requires consistent effort from both sides — but the alternative is accepting unnecessary and avoidable risk as a permanent feature of the delivery pipeline.

The Security Problem DevOps Created

DevOps environments are defined by speed and impermanence. Microservices spin up and tear down in seconds. Containers are ephemeral by design. Virtualized workloads shift constantly across infrastructure. This velocity is exactly what makes DevOps valuable — and exactly what makes traditional security tooling ineffective.

Traditional security tools were designed around static assets: physical servers, fixed IP addresses, persistent processes. Log aggregation and packet capture assume that the thing you are monitoring will still be there when you go back to look at it. In a container-native environment, that assumption breaks down entirely. A container that lives for thirty seconds may leave behind no forensically useful artifact at all.

At the same time, compliance frameworks have not kept pace. Regulations written around signature-based detection and log retention sit awkwardly alongside modern protocol mandates such as TLS 1.3’s requirement for perfect forward secrecy and PCI DSS directives to deprecate older SSL algorithm suites. Security teams are squeezed between legacy regulatory requirements and a rapidly evolving technical reality.

Supply chain complexity compounds the problem further. Most organizations rely on a web of third-party service partners to complete any meaningful business process. Each partner makes independent decisions about technology, staffing, and tooling. Forensic evidence from a partner environment is rarely shared unless a strong SLA or legal obligation forces the issue.

From Asset-Centric to Service-Centric Security

The traditional security operations model focuses on individual assets: a server, a database instance, a network device. The question asked is, “Is this asset compromised?” In a DevOps world, that question is increasingly unanswerable for any single ephemeral component — and increasingly irrelevant to the business impact of a breach.

A more useful question is: “Is this service behaving as expected?” Service-centric security shifts the unit of protection from an individual asset to the entire collection of microservices, processes, API calls, and infrastructure components required to complete a transaction or deliver a user experience. If a payment checkout flow requires three microservices, two queues, a caching layer, and a third-party fraud-detection API, then the security boundary is that entire graph — not any single node within it.

This reframing has practical consequences for how SecOps teams allocate attention, tooling, and budget:

• Discovery becomes service-aware. Rather than scanning for individual hosts, teams map which dynamic components belong to which critical services — including components that may not exist yet but will be instantiated on demand.
• Monitoring is tiered by service criticality. Critical services receive proactive behavioral monitoring, fine-grained analytics, and tighter detection and response SLAs. Less critical services receive lighter-weight coverage.
• Incidents are evaluated in service context. An anomaly in an isolated development container carries very different risk than the same anomaly in a component of a revenue-generating checkout service.

Retooling to Support the Digital Service Mission

Implementing service-centric security requires new integration patterns, but the DevOps ecosystem itself makes this more tractable than it might appear. Several trends in DevOps infrastructure work in the security team’s favor:

• Integration points and hooks. Modern DevOps platforms — CI/CD pipelines, service meshes, container orchestrators — expose rich APIs and event streams. Security tooling can attach to these integration points to observe service behavior without relying on traditional host-based agents that may not survive container lifecycle events.
• Orchestration breaks down silos. Container orchestration platforms such as Kubernetes maintain authoritative records of what is running, where, and as part of which workload. This gives security teams a continuously updated inventory of service components that would have required manual effort to maintain in a traditional environment.
• Automation is now culturally acceptable. DevOps culture has normalized automation across engineering and operations. Security teams can now apply automated response playbooks — isolating a compromised container, rotating a credential, blocking a service endpoint — without facing the cultural resistance that once slowed security automation adoption.

Data Retention: A Change That Can Save Money

One underappreciated benefit of service-centric security is the opportunity to rationalize data retention costs. Full packet capture and comprehensive log retention across every ephemeral workload in a large DevOps environment is prohibitively expensive and produces enormous volumes of low-signal data.

Tiering data management by service criticality addresses this directly:

• Critical services retain full packet data, detailed event logs, and behavioral metrics for the periods required by compliance frameworks and internal policy.
• Non-critical services retain only metadata or aggregated metrics, and for shorter windows — weeks or months rather than years.
• Compliance scope becomes more manageable. PCI DSS, for example, allows different monitoring and audit requirements for in-scope versus out-of-scope systems. If security teams know precisely which dynamic service components fall within the cardholder data environment, they can apply full retention to those components and reduce overhead everywhere else.

This approach does not eliminate log retention obligations — existing regulatory requirements still apply and teams should consult their compliance and legal teams before changing retention policies. But it provides a principled basis for making retention decisions rather than retaining everything indefinitely.

Better Value from Machine Learning

Machine learning-based anomaly detection is computationally expensive. Training models on undifferentiated telemetry from thousands of ephemeral workloads — most of which are irrelevant to any meaningful security outcome — wastes compute resources and degrades model quality. Garbage in, garbage out applies to security ML just as it does elsewhere.

When ML models are trained and evaluated against the behavioral baseline of a well-defined critical service, several things improve:

• False positive rates drop. The model learns what normal looks like for a specific, bounded service graph rather than across a noisy, heterogeneous environment.
• Models converge faster. A narrower, higher-quality training set means models reach useful accuracy more quickly.
• Alerts carry more context. An anomaly flagged within a specific critical service is immediately actionable — SecOps analysts know which business process is at risk and which team owns the affected components.

Conclusion: Leveraging DevOps Advances Without Sacrificing Risk Posture

DevOps has delivered real gains in delivery speed, infrastructure reliability, and organizational agility. It has also introduced genuine security blind spots: ephemeral workloads that outpace traditional monitoring tools, distributed service graphs that span organizational boundaries, and compliance frameworks still catching up to the new reality.

Service-centric security is not a product category or a single tool. It is a shift in how security operations teams frame their mission — from protecting individual assets to protecting the end-to-end services that the business depends on. That shift enables smarter resource allocation, more relevant machine learning, more defensible compliance postures, and ultimately a security model that is coherent in a world where the infrastructure itself is ephemeral.

The infrastructure changes that DevOps has forced on security teams also provide the hooks, the orchestration primitives, and the cultural acceptance of automation that make service-centric security achievable. The challenge is not the tooling. It is making the conceptual transition from asset to service as the fundamental unit of security concern.

App developers are often accused of treating security as an afterthought, while security teams are criticized for acting as gatekeepers that block or slow cloud migration. The reality is more nuanced. When properly integrated into the continuous-delivery pipeline, IT security becomes a business enabler rather than a bottleneck. The discipline that merges these worlds — sometimes called DevSecOps — depends on deliberate collaboration between DevOps and SecOps practitioners.

Why DevOps and SecOps Clash

The tension is structural. DevOps teams optimize for speed and continuous deployment. SecOps teams optimize for risk reduction and compliance. In traditional organizations, these goals were balanced through slow, sequential handoffs — development built the thing, then security reviewed it before release. Continuous delivery breaks that model entirely, pushing code to production in hours or days rather than weeks or months.

When business owners drive cloud initiatives directly, bypassing IT service managers and security teams, the result is shadow IT: cloud workloads that may not meet hardening standards, lack proper access controls, or fall outside regulatory compliance frameworks such as PCI DSS or HIPAA. Breaches caused by misconfigured cloud environments and unpatched applications are well-documented in public incident records. Closing these gaps requires both cultural change and process integration.

Five Measures to Align DevOps and SecOps

1. Inclusion: Bring Security Into the Design Phase

Security requirements are cheapest to satisfy when they are addressed at the architecture stage, not discovered during a pre-release audit. Cloud architects should involve security engineers when designing environments, ensuring that virtual machine configurations are hardened to recognized benchmarks such as the Center for Internet Security (CIS) controls. A secure software development lifecycle (S-SDLC) embeds security reviews directly into the sprint cycle, so vulnerabilities are identified and remediated as code is written rather than accumulated until release day.

2. Flexibility: Help Security Teams Understand Modern Architectures

Microservices and containers represent a fundamentally different threat model than the classic three-tier data center. Security teams that have spent years protecting monolithic applications need time and training to adapt. Containers, when properly configured with minimal base images and enforced network policies, can actually reduce the attack surface compared with traditional virtual machines. However, the tooling ecosystem for container-native security is still maturing, and security teams should be given both the flexibility to learn and the resources to evaluate tools designed for this architecture, such as image scanners, runtime threat detection agents, and service mesh policy engines.

3. Understanding: Developers Must Know the Compliance Landscape

Regulatory frameworks such as PCI DSS, HIPAA, SOC 2, and ISO 27001 place concrete, auditable requirements on how data is stored, transmitted, and accessed. Application teams that design cloud deployments without this knowledge can inadvertently create architectures that are expensive or impossible to certify. A practical step is to include compliance representatives in sprint planning or backlog grooming sessions, so that user stories include acceptance criteria tied to relevant controls. This turns compliance from a post-hoc review into a shared definition of done.

4. Progression: Adopt Static and Dynamic Application Security Testing

An S-SDLC is only as effective as the testing it incorporates. Static application security testing (SAST) analyzes source code for known vulnerability patterns without executing the application. Dynamic application security testing (DAST) probes running applications for exploitable weaknesses, simulating the behavior of an external attacker. Integrating both into the CI/CD pipeline — so that every pull request triggers a SAST scan and every staging deployment triggers a DAST run — gives development teams rapid feedback and reduces the volume of vulnerabilities that reach production.

5. Adoption: Use Agile Cadence as a Shared Security Rhythm

Two-week sprint cycles, a cornerstone of many agile shops, create a natural patch cadence that benefits both development and security goals. Security teams can schedule patch deployments and configuration updates within the same delivery pipeline that development uses, removing the friction of separate, ad hoc change windows. Frequent patching also disrupts the window of opportunity available to attackers exploiting known vulnerabilities — a key principle in reducing mean time to remediate (MTTR) for disclosed CVEs.

Building a DevSecOps Culture

Process changes alone are insufficient without cultural alignment. Leaders in both DevOps and SecOps should establish shared metrics — such as vulnerability backlog age, patch coverage, and mean time to detect — that give both teams a common view of security posture. Joint incident retrospectives, where both development and security engineers review what went wrong and why, are particularly effective at breaking down the blame culture that often hardens the divide between teams.

Organizations that embed security engineers directly into product squads (sometimes called “security champions” programs) report faster remediation cycles and higher developer security awareness. This model distributes security knowledge throughout the engineering organization rather than concentrating it in a siloed team that must review every release.

The Business Case for Collaboration

Many publicly disclosed breaches trace back to preventable root causes: unpatched software, misconfigured storage buckets, overly permissive IAM policies, or hard-coded credentials in source repositories. These are precisely the categories that a well-integrated DevSecOps pipeline is designed to catch. Beyond avoiding breach costs, organizations that treat security as part of continuous delivery can move faster with confidence, because they are not accumulating technical security debt that will eventually require an expensive remediation project or, worse, a breach response.

The cloud is not going away, and neither is the regulatory environment that governs it. DevOps and SecOps teams that invest in collaboration now will find themselves better positioned to deliver, defend, and comply as both technology and threat landscapes continue to evolve.

Due to the dynamic nature of DevOps and the business “secrets” they have access to, security vendor CyberArk has highlighted the importance of ensuring these teams are protected from the threat landscape.

According to Jeffrey Kok, senior director of solution engineering for Asia Pacific and Japan at CyberArk, exposing DevOps to the elements means privileged account credentials — such as SSH keys, API keys, and other credentials — are proliferating throughout IT infrastructure at a rapid-fire pace, creating massive security risks for organisations.

The CyberArk Advanced Threat Landscape 2018 highlights that 75 percent of security respondents reported their organisation has not implemented a privileged account security solution for DevOps.

This is potentially problematic when 60 percent of the DevOps respondents said they store privileged account or administrative passwords in a document on a company PC or laptop.

52 percent of DevOps respondents said they rely on the native secrets functionality of their cloud or DevOps vendors to be protected.

“This is potentially a risky approach because it creates separate security silos that are difficult to manage with an overall security policy,” the report says.

As respondents were able to provide more than one answer, 50 percent also said they employ a paid-for secrets solution; while 37 percent said they use systems built from open-source software.

“Most of the time the security guys get brought in when the applications are going to go live,” Kok told ZDNet.

43 percent of respondents confirmed that the security team is always brought in at the end of each development cycle, with CyberArk noting this may be adequate only if the length of a sprint averages a week or so.

While Kok said it might seem a “little bit daunting” for organisations to bring the security talent into the development process earlier, once the concept is embraced, he said, the end result is a much better user experience.

“That is one of the key gaps,” he explained.

The most effective business strategy will demand that security and DevOps work closely, which is why Kok pitched the idea of “SecOps” — where the application is designed with operation, but also with security in mind.

“Security by design,” Kok reiterated. “The most effective strategy will demand that security and DevOps work closely together from the very beginning and throughout development, testing, and deployment.”

The report said that as DevOps is a relatively new discipline, it is not entirely surprising that respondents report a lack of integration between DevOps and security teams.

CyberArk said that while collaboration varies by industry, it was found the closer partnerships between DevOps and security are most often found in consumer services and technology and telecommunications segments. The financial services organisations reported slightly below-average collaboration, and only 16 percent of healthcare respondents said their security and DevOps teams were “well integrated”.

“Today when everything is on the cloud and you have a lot of automation tools, the DevOps have anywhere from 5-20 tools that are used and all of them contain secrets … just one will break the entire chain,” he explained. “The idea is to not have ‘islands of security’.

“If DevOps can bring two teams together, why not then SecOps just bring one more to the party.”

Although Kok is focused mostly on the APJ region, he said the issues raised in the report cover DevOps teams globally.

However, as the APJ region is a little bit later to the DevOps game, it’s more imperative for the region to learn from the mistakes of others.

“We have the benefit of hindsight we can avoid the pitfalls,” he said.

Kok said the report was commissioned following a lot of déjà vu CyberArk was experiencing with customers concerned over the security of their organisation’s secrets.

The survey was conducted on behalf of CyberArk by market research firm Vanson Bourne during September and October 2017, and saw more than 1,000 IT security decision makers, DevOps, app developer professionals, and line of business owners across seven countries probed on their company’s practices.

PREVIOUS AND RELATED COVERAGE

What is DevOps? An executive guide to agile development and IT operations

To make the most of today’s containers, servers, virtual machines, and clouds, you need to deploy DevOps in your enterprise. Or, you can let your rivals put you out of business. It’s your choice.

DevSecOps: What it is and how it can help you innovate in cybersecurity

DevSecOps is like DevOps, but with security principles baked in. Here’s a quick guide to the basics, and how to incorporate it in your company.

Time to move on from DevOps and continuous delivery, says Google advocate

CI/CD, DevOps are so 2007. IT leaders and professionals need to elevate their thinking to guiding their businesses through disruptive times.

DevOps: The smart person’s guide (TechRepublic)

This comprehensive guide covers DevOps, an increasingly popular organizational structure for delivering rapid software deployments in the enterprise.

The Silo Problem: Fraud, Security, and Ops Operating in Isolation

In most enterprises, anti-fraud, security, and authentication teams have historically operated as separate domains. IT Operations sits even further away from these functions. The reasons are familiar to anyone who has worked inside a large organization:

• Silo mentality: individual teams optimise for their own discipline rather than shared outcomes.
• Political momentum: entrenched reporting structures make reorganisation difficult.
• Decentralised vendors: different third-party providers serve each function, deepening the divide.
• Low urgency (historically): the cost of poor coordination was invisible until a breach or a compliance deadline made it painfully visible.

In a typical siloed enterprise, the CISO and IT security team own threat detection and controls. Risk and compliance executives own anti-fraud and data protection. A separate Ops team — often reporting to the CIO or even the CFO — owns infrastructure and service delivery. These groups rarely collaborate unless a crisis forces them to.

Why Siloed Collaboration Breaks Down

The problem isn’t animosity between teams. Most security, fraud, and ops professionals share the same ultimate goal: protect the business and serve customers safely. The friction comes from structural misalignment:

• Inconsistent communication cadences — teams only synchronise reactively, not proactively.
• Conflicting incentives — security teams are measured on risk reduction; ops teams on uptime and velocity; fraud teams on loss rates. These metrics can pull in opposite directions.
• Duplicated tooling — each silo procures its own monitoring, alerting, and analytics stack, creating blind spots at the boundaries.
• Crisis-only coordination — collaboration that only happens during incidents is too slow, too expensive, and too late.

How DevOps and SecOps Show the Way

DevOps and SecOps started as entirely separate movements. DevOps grew from the frustration of developers and operations engineers working against each other; SecOps emerged from the recognition that bolting security onto finished software was both expensive and ineffective. Over time, practitioners in both camps discovered that their goals were complementary and that shared tooling, shared metrics, and shared culture produced better outcomes than parallel silos.

The result was DevSecOps: a model in which security practices are embedded into every stage of the software delivery pipeline rather than applied as a final gate. Threat modelling happens at design time. Static analysis and dependency scanning run in CI. Security engineers sit in sprint planning. Incident response feeds back into development priorities.

The same logic applies to the broader convergence of fraud prevention, authentication, and security operations. These disciplines share data, share adversaries, and share customers. Treating them as independent functions means each team sees only part of the picture — and attackers exploit the gaps between teams.

A Converged Model: Fraud, Authentication, and Security Together

The practical case for convergence rests on several pillars:

1. Shared Signals, Better Decisions

Fraud signals (unusual transaction patterns, device fingerprint anomalies) and security signals (credential stuffing attempts, anomalous API calls) often describe the same attack from different angles. A unified data pipeline lets analysts correlate these signals in real time rather than comparing reports after the fact.

2. Consistent Authentication Across Channels

When authentication is owned by multiple teams, the customer experience becomes inconsistent: a user might face different friction levels on mobile versus web versus call-centre interactions. An omnichannel authentication model — where a single policy engine governs risk-based authentication regardless of channel — requires fraud, identity, and security teams to define policy together and share the same risk signals.

3. Regulatory Pressure

Regulations such as the EU’s General Data Protection Regulation (GDPR) and similar data protection frameworks in other jurisdictions require organisations to demonstrate a holistic understanding of where personal data lives, how it is protected, and who can access it. Fragmented teams with fragmented tooling struggle to produce the unified data inventory and control evidence that regulators expect. A converged team, sharing a common asset register and control framework, is structurally better positioned for compliance.

The Real Obstacles to Convergence

Acknowledging that convergence is desirable is the easy part. Executing it is harder. The main barriers are not technical:

• Reporting structure: whose budget funds the converged team? Who does the unified function report to — the CISO, the CRO, the CIO?
• Cultural inertia: professionals who have built careers in a specialist discipline may resist joining a broader team with blurred boundaries.
• Process complexity: convergence can easily become a waterfall-style reorganisation project that takes two years to deliver and arrives too late. An agile, incremental approach — shared tooling first, shared metrics next, shared team structures last — is more likely to succeed.
• Vendor fragmentation: existing contracts and integrations with specialist vendors in each domain create switching costs and integration work.

Applying Agile Principles to Organisational Convergence

The DevOps movement succeeded, in part, because it did not begin with a grand reorganisation. It began with small, cross-functional teams experimenting with shared tooling and shared metrics — and proving value in short cycles. The same approach applies here:

1. Start with a shared data layer. Integrate fraud signals, authentication events, and security telemetry into a common SIEM or data lake so all teams can query the same source of truth.
2. Define shared KPIs. Agree on metrics that span team boundaries — for example, time-to-detect an account takeover, or false-positive rate on authentication challenges.
3. Run joint incident reviews. When a fraud event or security incident occurs, include all relevant disciplines in the post-mortem. This builds mutual understanding faster than any formal reorganisation.
4. Evolve structure to follow culture. Once teams trust each other and share goals, structural changes (shared reporting lines, unified budgets) are much less disruptive.

The Bottom Line

DevOps demonstrated that development and operations teams produce better software faster when they share goals, tooling, and culture rather than throwing work over a wall. SecOps demonstrated that security is more effective when it is embedded in delivery pipelines rather than applied at the end. The same logic now applies to the broader convergence of fraud prevention, authentication, security operations, and IT ops.

The threat environment is escalating. Regulatory expectations are rising. Customer tolerance for friction caused by inconsistent authentication is falling. Organisations that break down the silos between these disciplines — incrementally, with an agile mindset, and without waiting for a perfect organisational blueprint — will be better positioned to detect attacks earlier, respond faster, and deliver a seamless experience to their customers.

The tools and the talent already exist inside most enterprises. What is required is the willingness to set aside political boundaries and build the shared context that makes genuine collaboration possible.

DevOps Is All About Speed—But Speed Has a Cost

A quick search on the term “DevOps” reveals a consistent theme: speed. Proponents praise it as a faster path to market, a catalyst for innovation, and an engine for organizational agility. Through continuous integration, rapid infrastructure delivery, and tight feedback loops with customers, DevOps has empowered startups and established enterprises alike to outpace slower-moving competitors.

As a business and technology model, the value is hard to argue with. DevOps delivers real results for organizations willing to align their product development and IT delivery around its principles. But that relentless focus on speed has a well-documented downside: security often gets left behind.

When Speed Outpaces Security

When a team is racing to ship new releases and beat competitors to market, thorough security reviews can feel like a bottleneck. Detailed threat modeling, compliance checks, and vulnerability assessments take time—time that development teams under pressure are reluctant to spend. The result is shortcuts, quick fixes, and security gaps that can introduce serious vulnerabilities into production systems.

This tension between velocity and security is not theoretical. A 2016 study by digital certificate company Venafi found that 79% of CIOs surveyed expected the speed of DevOps to make it harder to distinguish trusted from untrusted systems and processes. That statistic underscores a structural problem: the faster you deploy, the harder it becomes to maintain a clear picture of your security posture.

Why SecOps Is Often Unprepared

Part of the blame lies with the security function itself. Many security tools and processes in widespread use today were designed for an earlier era of computing—built around static, on-premises architectures with well-defined perimeters. These legacy approaches tend to be proprietary, resource-intensive, and slow to adapt.

In cloud-native and hybrid environments, that model breaks down. Modern infrastructure is ephemeral, loosely coupled, and continuously changing. Security operations need to match that reality with solutions that are:

• Agile — capable of adapting to rapid infrastructure changes
• Lightweight — able to run without heavy resource overhead in containerized or serverless environments
• Loosely coupled — designed to integrate with modern CI/CD pipelines rather than act as a gate outside them
• Extensible — open to integration with the wide variety of tools that DevOps teams already use

Most SecOps teams have evolved slowly and, as a result, find themselves poorly positioned to support the pace and architecture of contemporary DevOps workflows.

How DevOps Teams Can Expand Their Security Awareness

One practical step for DevOps teams is to embed security thinking directly into their normal workflow rather than treating it as a separate, downstream concern. This means:

• Revisiting the original threat model whenever the environment changes significantly—for example, when migrating workloads to a new cloud provider or adopting a new microservices architecture.
• Evaluating new data and new features against the compliance requirements and security controls established during initial development, rather than treating them as an afterthought.
• Updating defense strategies to reflect the constraints and shared-responsibility model of cloud platforms.
• Implementing continuous monitoring and automated remediation so that security issues are caught and addressed in near-real time rather than discovered weeks later in a manual audit.

From Adversaries to Allies: The Rise of DevSecOps

The good news is that the wall between DevOps and SecOps is beginning to come down. Forward-thinking organizations are moving away from the traditional requestor/approver relationship—where developers submit work and security teams act as a separate approval gate—toward genuine collaboration between the two disciplines.

This cultural and operational shift has a name: DevSecOps. Rather than bolting security onto the end of the delivery pipeline, DevSecOps integrates security practices throughout the entire software development lifecycle. The model aims to:

• Detect security problems earlier—ideally during development, before code reaches production
• Enable faster response to threats through automated tooling embedded in CI/CD pipelines
• Give security teams visibility into continuously changing cloud environments without slowing delivery
• Align security goals with product delivery goals so the two functions reinforce rather than obstruct each other

What DevSecOps Looks Like in Practice

Implementing DevSecOps is as much a cultural change as a technical one. Some practical approaches organizations use include:

• Shifting security left — incorporating static analysis, dependency scanning, and secrets detection into the developer’s local workflow and pull-request process, not just at deployment time.
• Shared ownership of security metrics — making vulnerability counts and mean-time-to-remediate part of the team’s shared dashboard rather than a SecOps-only concern.
• Security as code — defining security policies, firewall rules, and compliance checks in version-controlled configuration files so they evolve alongside the application.
• Automated compliance gates — using policy-as-code tools to enforce controls automatically in the pipeline, reducing the need for manual security reviews of every release.
• Cross-functional security champions — embedding engineers with security expertise inside development teams to serve as a bridge between SecOps and DevOps culture.

The Bottom Line

DevOps is not going away, and neither are the security obligations that come with building and operating software at scale. The question is no longer whether security and DevOps can coexist—it’s how quickly organizations can bring them together. DevSecOps offers a practical framework for doing exactly that: preserving the speed and agility that make DevOps valuable while building the security intelligence needed to protect it.

Organizations that embrace this collaboration—treating security as a shared engineering concern rather than a compliance checkbox—will be better positioned to detect problems earlier, respond faster, and protect their customers and their business without sacrificing the delivery velocity that gives them a competitive edge.

The answer is not simply to hire more people — it is to make the people you have dramatically more effective. Organizations that embed security thinking across their engineering and operations teams multiply the impact of every security hire. Below are five concrete tips that help you do exactly that.

1. Get Your DevOps Team On Board With SecOps

If your development and operations teams are already working in a DevOps model, the next logical step is to extend that collaboration to security — commonly called DevSecOps or SecOps. Train the people who write and deploy code to treat security as part of their everyday responsibility, not as someone else’s problem to clean up later.

This cultural shift must be driven from leadership. When security metrics appear on the same dashboards as deployment frequency and uptime, engineers begin to internalize security as a first-class concern. Reinforce that expectation in code-review checklists, sprint retrospectives, and performance evaluations.

2. Bring Security Alerts Into DevOps Workflows

Asking developers to log into a dedicated security console they rarely visit almost guarantees that alerts will be ignored or actioned too slowly. Instead, deliver security signals where engineers already spend their time.

ChatOps platforms such as Slack, Microsoft Teams, or Mattermost are an effective bridge. Routing security alerts into a dedicated channel — or even into team-specific channels — means engineers see relevant alerts in context, can discuss them immediately, and can escalate or dismiss without context-switching to an unfamiliar tool.

The lower the friction, the more consistently your team will respond to alerts in a timely and repeatable way. Enrich alerts with contextual metadata — the host, the process, the user account involved — so the people reviewing them can triage quickly without needing deep security expertise.

3. Extend Security Visibility Across the Whole Organization

Do not stop at the engineering team. When security awareness tools and channels are accessible to everyone — product managers, support staff, finance — you gain a much larger set of eyes watching for anomalies.

Practical ideas include:

• A company-wide #security Slack channel where anyone can flag suspicious emails, unusual login prompts, or strange behavior.
• Automated alerts that ping the individual user whose action triggered a security event, asking them to confirm or deny — this dramatically speeds up triage.
• Regular, short security digests that keep the broader team informed about active threat patterns without overwhelming them.

Building this culture of openness and shared responsibility makes your organization a harder target and reduces the burden on your dedicated security staff to investigate every false positive alone.

4. Educate and Train the Entire Team

Workflow integration only goes so far if people do not understand what they are looking at. Basic security training for every employee — not just engineers — is a high-leverage investment. Key topics to cover include:

• Phishing and social engineering: How to recognize suspicious emails, links, and requests, and what to do when you spot them.
• Authentication hygiene: How to use multi-factor authentication (MFA), password managers, and single sign-on (SSO) tools correctly.
• Network safety: Which networks are safe to use, when to use a VPN, and how to stay secure while traveling for work.
• Physical security basics: Locking screens when stepping away, never plugging in unfamiliar USB devices, and following a clean-desk policy.

The goal is to raise the organization’s collective security IQ to a level that makes you an unappealing target. A well-trained team generates fewer incidents, which directly reduces the workload on your security staff.

5. Choose Comprehensive, Integrated Security Tools

Tool sprawl is a silent productivity killer for security teams. When alerts come from a dozen disconnected systems, analysts spend more time correlating data than actually responding to threats. Aim for a single-pane-of-glass approach: one platform — or a tightly integrated set of platforms — that gives your security team a unified view of your network, infrastructure, and application layer.

When evaluating security platforms, look for native integrations with the tools your teams already rely on, such as:

• Incident management tools (e.g., PagerDuty, VictorOps / Splunk On-Call)
• Chat and collaboration platforms (e.g., Slack, Microsoft Teams)
• Cloud provider consoles and APIs (e.g., AWS Security Hub, Azure Defender, GCP Security Command Center)
• CI/CD pipelines, so that security scanning happens automatically at build time

A unified toolset enables proactive vulnerability management: your team can identify and remediate weaknesses before they are exploited, rather than scrambling to contain a breach after the fact. Over time, it also builds an institutional knowledge base about your security posture and how your systems and users behave under normal conditions — making anomalies far easier to spot.

Putting It All Together

Security talent will continue to be expensive and difficult to hire for the foreseeable future. Rather than waiting for the market to change, forward-thinking organizations are restructuring how security work gets done — distributing ownership across development, operations, and the entire business, and equipping everyone with the tools and knowledge they need to contribute.

By embedding security into existing DevOps workflows, training your full team, and consolidating your tooling, you give your dedicated security professionals the leverage they need to operate strategically rather than reactively. The result is a more resilient organization and a security team that can actually scale with the business.

Originally sourced from business2community.com.