PALO ALTO, Calif.–(BUSINESS WIRE)–Data Theorem, Inc., a leading provider of modern application security, today announced that its Cloud Secure cloud-native security solution has won the 2021 Fortress Cyber Security Award. The industry awards program identifies and rewards the world’s leading companies and products that are working to keep our data and electronic assets safe among a growing threat from hackers.

Cloud Secure is the industry’s first solution delivering attack surface management security for cloud-native applications that starts at the client layer (mobile and web), protects the network layer (REST and GraphQL APIs), and extends down through the underlying infrastructure (cloud services). Its combination of attack surface management and defensive protections enables both offensive and defensive security capabilities to best prevent data breaches of cloud-native applications and serverless cloud functions.

“We are so proud to name Data Theorem’s Cloud Secure as a winner in the 2021 Fortress Cyber Security Awards program,” said Maria Jimenez, Chief Nominations Officer, Business Intelligence Group. “As our society continues to evolve and become more reliant on networks and data, companies such as Data Theorem are critical at providing the protection and trust consumers demand.”

For information about the annual Fortress Cyber Security Awards, please visit https://www.bintelligence.com/fortress-cyber-security-awards.

“Looking at Data Theorem’s long list of analyst and industry award honors, it is particularly gratifying to be recognized in programs such as Business Intelligence Group’s Fortress Cyber Security Awards program, where winners are determined by business executives that have experience and knowledge in the industry,” said Doug Dooley, Data Theorem COO. “Cloud Secure shined against the competition in the program’s unique scoring system for its unique innovations securing public cloud environments and the enterprise applications that depend on those cloud services. Organizations turn to Data Theorem to protect their cloud applications due to the benefits of our dynamic and run-time analysis with offensive attack surface management and defensive protection toolkits. This enables organizations to conduct continuous, automated security inspection and remediation of their most important cloud-native applications.”

Data Theorem’s broad AppSec portfolio protects organizations from data breaches with application security testing and protection for modern web frameworks, API-driven microservices and cloud resources. Its solutions are powered by its award-winning Analyzer Engine, which leverages a new type of dynamic and run-time analysis that is fully integrated into the CI/CD process, and enables organizations to conduct continuous, automated security inspection and remediation.

Business Intelligence Group
The Business Intelligence Group was founded with the mission of recognizing true talent and superior performance in the business world. Unlike other industry award programs, these programs are judged by business executives having experience and knowledge. The organization’s proprietary and unique scoring system selectively measures performance across multiple business domains and rewards those companies whose achievements stand above those of their peers.

Data Theorem
Data Theorem is a leading provider of modern application security. Its core mission is to analyze and secure any modern application anytime, anywhere. The award-winning Data Theorem Analyzer Engine continuously analyzes APIs, Web, Mobile, and Cloud applications in search of security flaws and data privacy gaps. Data Theorem products help organizations prevent AppSec data breaches. The company has detected more than 1 billion application eavesdropping incidents and currently secures more than 8,000 modern applications for its enterprise customers around the world. Data Theorem is headquartered in Palo Alto, Calif., with offices in New York and Paris.

Complex hypervisor software helps run cloud computers, but verifying its security is often thought to be nigh impossible. Now computer scientists at Columbia University have developed what they say is the first hypervisor that can guarantee secure cloud computing.

Hypervisors organize cloud servers into virtual machines to supply data and computing power over the Internet. Hacks that successfully exploit hypervisor vulnerabilities could gain unfettered access to the data of millions of customers of cloud computing providers such as Amazon.

“All it takes is a single weak link in the code — one that is virtually impossible to detect via traditional testing — to leave a system vulnerable to hackers,” says Ronghui Gu, a computer scientist at Columbia University’s School of Engineering and Applied Science and co-author on the researchers’ published study about the work.

In theory, scientists can formally verify software to mathematically prove that its code “protects data security under 100% of circumstances,” Gu says. However, most verified hypervisors are often far simpler than their commercial counterparts, since they are specifically designed for verification instead of practical applications. In contrast, modern commercial hypervisors are huge pieces of software, often including an entire operating system kernel, which can make verifying them a seemingly insurmountable task.

For example, it took three person-years to verify 6,500 lines of code with the CertiKOS hypervisor and 10 person-years to verify 9,000 lines of code with the seL4 hypervisor, both of which were designed for verification. In comparison, the widely used KVM open-source hypervisor, a full-featured multi-processor system integrated with Linux, has more than 2 million lines of code.

Now the Columbia computer scientists have developed a way to verify commercial-grade hypervisors. They used their new technique to develop a secure version of KVM named SeKVM, which they suggest is the first machine-checked formally verified commercial-grade hypervisor.

The researchers dubbed their new technique microverification, which reduces the amount of work needed to verify a hypervisor. It breaks down a hypervisor into a small core and a set of untrusted services, and then goes on to prove the hypervisor secure by verifying the core alone. The core has no vulnerabilities for a hack to exploit, and this core mediates all the hypervisor’s interactions with virtual machines, so even if a hack undermines one virtual machine, it does not compromise the others.

Based on microverification, the scientists developed software named MicroV to verify large commercial-grade multi-processor hypervisors. With the help of MicroV, they developed a secure core for SeKVM only 3,800 lines of code long, which they verified over the course of two person-years.

When it comes to real application workloads, SeKVM performed similarly to unmodified KVM, at most incurring less than 10% performance overhead on native hardware KVM was specifically designed to run on. At the same time, SeKVM supported KVM’s wide range of features.

“SeKVM is just KVM with some minor changes,” says study co-author Jason Nieh, a computer scientist at Columbia University’s School of Engineering and Applied Science. “It performs like regular KVM, inherits the functionality of regular KVM, but on top of that, provides security guarantees that regular KVM does not have.”

In the future, “we will keep exploring this concept of cyber-resilient systems software and build safeguards in various domains, from banking systems and Internet of Things devices to autonomous vehicles and cryptocurrencies,” Gu says. “SeKVM will lay a foundation for future innovations in systems verification and lead to a new generation of cyber-resilient systems software. In a world where cybersecurity is a growing concern, this resiliency is in high demand.”

{mosads}If you have been considering options that will allow you to host your company’s infrastructure on the cloud, it can be hard to know where to start. That said, this transition can ultimately lead to the ability to host your IT infrastructure much more safely and securely.

Luckily, The 2021 Amazon Web Services Certification Training Bundle is on sale for $59.99 down from $2,500, at a 97% discount. The bundle offers 40 hours of content that will train you on the technical essentials, architectural principles, the cloud, and more. The courses will be taught by Certs School, which provides educational resources that are designed to let students learn from industry professionals with years and years of experience.

The courses will introduce you to the fundamentals of Amazon Web Services (AWS), which is a cloud platform that companies use to safely host their infrastructure. The course will walk you through the terminologies, concepts, benefits, and best deployment options for your company. You will also master architectural principles and become a certified AWS solutions architect.

The courses will also provide you with exposure to the cloud platform, which will help you attain valuable expertise in launching, managing, and continuing to operate systems that are completely fault-tolerant. Finally, the course bundle will walk you through Puppet, which is a tool that allows you to automate your IT infrastructure across the entire network. It will walk you through how you can use Puppet for scalability, redundancy, performance, and more.

This deal will provide you with lifetime access that can be used on desktop or mobile devices. Right now, the Amazon Web Services course bundle is on sale for only $59.99.

That this year has been an interesting one for us in IT is an understatement. It’s been a very good year for public cloud though, with record growth in AWS and Azure. And for many businesses, the “digital transformation” journey towards the cloud has been highly accelerated. Maybe last year’s small proof-of-concept Azure deployments have grown into full-scale production resources. Or the inability to access your co-lo datacenter servers due to COVID-19 restrictions has forced a migration of VMs to public cloud.

Whatever the reason, you may find yourself responsible for a lot more cloud Infrastructure-as-a-Service (IaaS) and Platform-as-a-Service (PaaS) resources and now taking stock of the security posture of all of it.

This article will look at the comprehensive guidance Microsoft offers for “how to do security” in Azure and how to use the tool that brings it all together: Azure Security Center (ASC).

Cloud Adoption Framework

The CAF is a good place to start, at any stage in your cloud journey, particularly for larger businesses. Based on cloud adoption best practices from Microsoft, partners and customers, it has guidance and tools to shape technology, people and processes for cloud success. It covers strategy and planning, migration, governing, management, organization and innovation. Here we’re going to focus on the security strategy, part which covers conceptS such as securing your cloud resources as well as using cloud technology to transform how you do security, along with the shared responsibility model.

Moving to the cloud means that some parts of security (physical hosts/network/datacenters) are now the responsibility of the provider, whereas other parts stay with you (data, endpoint devices, accounts and identities). Further, with IaaS you still have to secure the OS and applications in your VMs, apply network controls and manage your identity directory (Azure Active Directory — AAD). Using PaaS services shifts some of these responsibilities to the cloud provider, depending on the service you’re using. This is one reason that you should look to modernize your applications away from IaaS towards PaaS as there’s less for you to secure, but new features to understand to ensure a good security resilience.

The CAF also covers different admin security roles involved in managing cyberrisk and how they change when you’re in the cloud. More direct guidance is also on offer through a set of recommendations and a reference implementation.
The Well-Architected Framework, on the other hand, is a set of assessments building on these five pillars:

Cost Optimization
Operational Excellence
Performance Efficiency
Reliability
Security
The assessments ascertain where you’re at today, and then spits out a set of curated guidance to improve your approach.

Why Containers Have Become the Deployment Standard

Containers provide a portable, platform-agnostic way to package an application’s code, configuration, and dependencies into a single runnable unit. Unlike virtual machines, containers share the host operating system kernel, which means they start in milliseconds rather than minutes. This characteristic alone makes containers ideal for modern CI/CD pipelines, where rapid iteration and frequent deployments are the norm.

The key benefits driving adoption include:

• Portability: A container image runs consistently across laptops, on-premises servers, and public cloud environments, eliminating environment-specific configuration drift.
• Resource efficiency: Because containers do not boot a full OS, they consume significantly less CPU and memory than equivalent VMs, allowing higher workload density on the same hardware.
• Elastic scalability: Orchestration platforms such as Kubernetes can spin new container instances up—or retire them—in seconds in response to demand spikes, far faster than provisioning VMs.
• Developer velocity: Packaging dependencies inside the image shortens onboarding time and reduces “works on my machine” incidents, supporting faster release cycles.

The Security Challenges Containers Introduce

The same properties that make containers fast and flexible also make them harder to secure than traditional or even virtualised infrastructure. Three distinct problem areas stand out.

1. Density and Dynamism at Scale

A single physical host in a containerised environment may run dozens or hundreds of containers simultaneously. Those containers are created and destroyed continuously by schedulers such as Kubernetes. Legacy security controls—particularly network appliances and host-based agents—were designed for environments where workloads have stable IP addresses and long lifetimes. In a container environment, a workload may exist for only a few seconds before being replaced by a fresh instance with a different identifier. Security policies that rely on static IP addresses or persistent agents cannot keep pace with this cycle.

2. An Expanded and Automated Attack Surface

Container orchestration platforms expose rich APIs that automate scheduling, networking, storage, and secrets management. While these APIs are essential to the operational model, they also represent a significant attack surface. A misconfigured Kubernetes API server, an overly permissive service account, or an insecure container registry can allow an attacker to move laterally across many workloads simultaneously. The automation that makes containers efficient also means that a single misconfiguration can propagate instantly across an entire cluster.

3. Agent Incompatibility

Traditional endpoint security relies on agents installed inside each workload. In containerised environments this approach is impractical: containers are intentionally minimal (often based on distroless or Alpine images), ephemeral, and immutable at runtime. Installing and maintaining agents inside every container image conflicts directly with the immutable infrastructure model, inflates image sizes, and creates operational overhead that offsets the efficiency gains containers provide.

Principles of Effective Container Security

Addressing these challenges requires rethinking security architecture rather than retrofitting legacy tools. Several principles guide effective approaches:

Policy Driven by Application Context, Not Network Topology

Because container IP addresses are ephemeral, security policies must be expressed in terms of application identity—labels, namespace, service name, or workload role—rather than IP ranges or port numbers. Container orchestrators already maintain this metadata; effective security tools consume it directly from the control plane (for example, the Kubernetes API) and translate it into enforcement rules automatically as workloads start and stop.

Declarative, Pre-Defined Policies

Security policies should be defined before containers are instantiated, using a declarative model that describes the intended behaviour of an application tier (for example, “the payment service may only accept inbound connections from the API gateway, and may only initiate outbound connections to the database”). When a new container matching that application’s labels comes online, the policy is applied immediately without manual intervention.

Consistent Controls Across Hybrid and Multi-Cloud Environments

Most enterprises run workloads across a combination of on-premises infrastructure, private cloud, and one or more public cloud providers. Security controls that only apply to one environment create blind spots and management complexity. An effective container security platform applies a consistent policy model regardless of where the workload is running—whether on bare metal, in a VM, in a managed Kubernetes service, or in a platform-as-a-service environment.

Stateful, Application-Aware Enforcement

Simple packet filtering is insufficient for modern application threats. Security enforcement should be stateful—tracking connection state rather than evaluating each packet in isolation—and application-aware, understanding the semantics of protocols such as HTTP, gRPC, or database wire formats. This allows policies to distinguish between legitimate API calls and anomalous or malicious traffic at the application layer.

Practical Steps to Improve Container Security Today

Regardless of which platform or tooling you choose, the following practices form a baseline that every team running containers should implement:

1. Scan images at build time. Integrate a container image scanner into your CI pipeline to catch known CVEs in base images and installed packages before they reach production.
2. Use minimal base images. Prefer distroless or Alpine-based images to reduce the attack surface. Fewer packages mean fewer vulnerabilities.
3. Enforce least-privilege at the pod level. Set securityContext fields in Kubernetes manifests to run containers as non-root, drop unnecessary Linux capabilities, and use read-only root filesystems.
4. Apply Kubernetes Network Policies. Restrict pod-to-pod traffic using NetworkPolicy resources so that workloads can only communicate with explicitly permitted peers.
5. Protect the control plane. Enable Kubernetes RBAC, restrict access to the API server, rotate credentials regularly, and audit API server logs for anomalous activity.
6. Implement runtime monitoring. Use a runtime security tool capable of detecting anomalous system calls or network connections from running containers, providing a last line of defence against zero-day exploits and misconfigurations that slip through build-time checks.
7. Manage secrets securely. Never bake secrets into container images. Use a secrets management solution—such as Kubernetes Secrets with envelope encryption, HashiCorp Vault, or a cloud-provider equivalent—and inject secrets at runtime.

Can You Have Both Agility and Security?

The answer is yes—but only if security is treated as an architectural concern from the start rather than something bolted on after deployment. The fundamental shift is moving from a perimeter-based model, where security is enforced at the edge of the network, to a workload-centric model, where every container carries its own security context and policies follow the workload wherever it runs.

When security policies are expressed in terms that orchestrators already understand—labels, namespaces, service identities—and when enforcement is automated by tools that integrate directly with the control plane, the operational overhead of securing containers becomes manageable. Teams get the rapid deployment cycles they need, and security teams gain consistent, auditable visibility across the entire environment.

Container technology is maturing quickly. The ecosystem of open-source tools (Falco for runtime security, OPA/Gatekeeper for policy enforcement, Trivy for image scanning, among others) and commercial platforms that address these challenges is growing alongside it. Teams that invest in understanding the container threat model and adopt security tooling designed for ephemeral, high-density workloads will find that agility and security reinforce each other rather than competing.

This article was updated and expanded from an original post referencing coverage from datacenternews.us (2019) and commentary from vArmour. Product and company details should be independently verified as the container security vendor landscape has changed significantly since the original publication date.

The DevOps Promise: Speed, Scale, and Open Source

DevOps has fundamentally changed how organisations build and ship software. By combining cultural change with cloud-based infrastructure, RESTful APIs, and open-source tooling, teams can move from code commit to production deployment in hours rather than weeks. The efficiency gains are real — but they come with an underappreciated security cost.

Open-source software is a cornerstone of modern DevOps pipelines. Package managers, container base images, shared libraries, and third-party frameworks all accelerate development. The common assumption is that widely used open-source code is inherently safer because thousands of eyes are reviewing it. In practice, that assumption is dangerously incomplete.

Why Open Source Does Not Equal Automatically Secure

Community scrutiny is valuable, but it is not a substitute for active security management. Consider the following realities:

• Vulnerabilities are discovered continuously. Open-source forums and CVE databases are populated daily with newly disclosed exploits affecting popular packages. A library that was safe last month may carry a critical flaw today.
• Patches require human action. Even when a fix is published, it is up to your team to identify the affected dependency, test the patch, and deploy an update. In fast-moving pipelines this step is frequently skipped or delayed.
• Transitive dependencies multiply risk. A single npm or Maven package can pull in dozens of indirect dependencies. You may have a vulnerable library in your build without being aware it is there at all.
• Licensing obligations add a compliance layer. Beyond security, open-source components carry licensing terms (GPL, LGPL, MIT, Apache, and so on). Using the wrong licence in a commercial product can create legal exposure alongside security exposure.

The Threat Landscape Is Accelerating

Attackers have adopted the same automation and tooling that DevOps teams use to ship software faster. Automated scanners probe public-facing applications for known CVEs within hours of a vulnerability being disclosed. Supply-chain attacks — where malicious code is introduced into a legitimate open-source package — have become an established attack vector. The result is that the window between a vulnerability being announced and being actively exploited has shrunk dramatically.

Regulatory pressure compounds the technical risk. Frameworks such as the EU General Data Protection Regulation (GDPR) impose obligations around data security, and demonstrating compliance requires organisations to know what software they are running, what components it contains, and whether those components are up to date. That is difficult to do without a formal Software Bill of Materials (SBOM) and a continuous dependency-scanning process.

Two Common Failure Modes

Organisations that have not integrated security into their DevOps pipeline typically fall into one of two traps:

1. The blind spot. Security is treated as someone else’s problem. Development and operations teams ship software quickly, but nobody maintains a clear picture of which open-source packages are in use, which versions are running in production, or whether any carry known vulnerabilities. The organisation does not know it is at risk until something goes wrong.
2. The bottleneck. Security teams attempt to compensate with heavyweight manual review processes, gate-keeping releases with lengthy audits. This defeats the purpose of DevOps, reintroduces long feedback loops, and breeds friction between development and security — without necessarily producing better outcomes.

Integrating Security Into the DevOps Pipeline

The answer is not to slow DevOps down. It is to embed security controls at each stage of the pipeline so that security becomes a by-product of the delivery process rather than a gate at the end of it. This approach is often called DevSecOps.

Key Practices

• Software Composition Analysis (SCA). Automated tools scan your codebase and build artefacts to identify open-source components, their versions, known CVEs, and licence types. This produces an SBOM and surfaces risks before code reaches production.
• Static Application Security Testing (SAST). Analyse source code or compiled binaries for security flaws — injection vulnerabilities, insecure defaults, hard-coded credentials — as part of the CI build step, before a pull request is merged.
• Dynamic Application Security Testing (DAST). Run automated tests against a running application in a staging environment to detect issues that only manifest at runtime, such as authentication bypasses or improper error handling.
• Dependency pinning and lock files. Pin dependency versions in lock files (package-lock.json, Pipfile.lock, go.sum, etc.) to ensure reproducible builds and to prevent silent upgrades to compromised package versions.
• Automated patch management. Tools such as Dependabot or Renovate can open pull requests automatically when a new, patched version of a dependency is available, reducing the manual effort needed to stay current.
• Policy as code. Define security and licensing policies in machine-readable form and enforce them in CI/CD. A build that introduces a high-severity CVE or a prohibited licence simply fails, giving developers immediate, actionable feedback.

The Role of Application Security Specialists

Specialist vendors in the application security space — including CA Veracode, which co-produced the original webcast this article references — offer platforms that combine SCA, SAST, and DAST capabilities with integration points for popular CI/CD tools. The value proposition is that security data surfaces inside the developer’s existing workflow (IDE plugins, pull-request comments, pipeline gates) rather than in a separate security console that development teams rarely open.

Analyst input from firms such as Freeform Dynamics has consistently highlighted that the organisational and cultural barriers to DevSecOps are as significant as the technical ones. Security teams need to shift from a role of enforcer to one of enabler — providing guardrails and tooling rather than after-the-fact audits.

Practical Starting Points

If your organisation is at the start of this journey, consider these incremental steps:

1. Inventory what you have. Run an SCA tool against your repositories to establish a baseline. You cannot manage risk you have not measured.
2. Triage by severity and exposure. Not every CVE demands immediate action. Prioritise critical and high-severity vulnerabilities in components exposed to untrusted input.
3. Integrate scanning into CI. Even a single SAST or SCA scan in the CI pipeline is better than none. Expand coverage incrementally.
4. Set and document policy. Agree with stakeholders what constitutes an acceptable risk. Document it. Enforce it automatically where possible.
5. Educate developers. Security tools surface findings; developers fix them. Invest in secure coding training so that developers understand the issues the tools are flagging.

Summary

DevOps delivers better, faster, and cheaper software — but speed and cost savings are negated if the software shipped is insecure. Open-source components are both an accelerator and an attack surface. Organisations that embed security tooling directly into their CI/CD pipeline, maintain a clear picture of their software dependencies, and treat security as a shared responsibility across development, operations, and security teams are best positioned to realise the full benefits of DevOps without accepting unacceptable risk.

Static analysis tools help teams find defects in their own code, but they rarely cover the full dependency tree. This article explains three concrete steps—drawn from a DevSecOps framing—for closing that gap.

The Old Way: Late-Stage, Manual Security Reviews

For much of software engineering history, security assessment happened near the end of the development lifecycle—if it happened at all. Long waterfall or spiral-model release cycles meant that security reviews were scheduled events, usually triggered by an external audit or a customer contractual requirement rather than by engineering discipline.

Several factors compounded the problem:

• Infrequent reviews meant known defects aged in backlogs without remediation pressure.
• Feature priority consistently outranked security fixes that “normal users would never see.”
• Lack of defensive-coding education meant many engineers had no practical training in writing secure code.
• Tool gaps left teams without automated ways to catalogue or assess open-source dependencies.

Information-security teams focused on producing findings reports to satisfy auditors rather than on preventing defects at the source. The result was a security posture that was reactive, not preventive.

The New Way: DevSecOps

DevOps collapsed the wall between software development and infrastructure operations by automating delivery pipelines and enabling teams to iterate continuously. DevSecOps applies the same philosophy to security: instead of a gate near the end of the pipeline, security controls are embedded throughout the software development lifecycle (SDLC).

The central principle is often called shifting left—moving security testing and open-source composition analysis earlier in the process, ideally into the design and development phases rather than pre-production or production. Defects found earlier are cheaper and faster to fix, and they are far less likely to reach end users.

Just as DevOps gives developers the tools to define and version infrastructure, DevSecOps gives them tools to own and act on security information without waiting for a centralised InfoSec team to run a periodic review.

Step 1: Start Security in the Design Phase

The fastest way for open-source risk to accumulate is to start a new project with whatever libraries happen to be on a developer’s workstation from the last project—possibly years out of date and carrying unpatched CVEs.

Before writing the first line of code on a new project, establish a bill of materials (BOM): an explicit, versioned inventory of every open-source framework and library the application will use. Then vet that list against known vulnerability databases.

Practical actions at design time:

• Choose the most current stable release of each framework rather than carrying forward a version from a previous project.
• Use software composition analysis (SCA) tools to generate and validate the BOM before significant development work begins. Many commercial and open-source options exist (the original article mentioned SourceClear, which has since been acquired; equivalent capabilities are available in tools such as OWASP Dependency-Check, Snyk, or GitHub’s native dependency graph).
• Define an acceptable policy for dependency freshness and known vulnerabilities before the project begins, not after.

Catching a problematic dependency at design time costs a conversation. Catching it in production costs an incident response.

Step 2: Automate Everything

Developers already carry significant cognitive load. Adding manual security checklists, approval workflows, or portal sign-offs to every deployment will reliably produce one outcome: developers will route around those controls.

The solution is to make security checks automatic, asynchronous, and invisible to the developer’s primary workflow. Security tooling should run in the CI/CD pipeline—triggered automatically on every commit or pull request—and surface actionable results without blocking the developer’s flow unless a critical threshold is crossed.

Key automation patterns:

• Static application security testing (SAST) runs against first-party code on every push.
• Software composition analysis (SCA) scans the dependency manifest on every build and alerts on newly disclosed CVEs, even for code that has not changed.
• Secrets scanning checks for accidentally committed credentials or API keys.
• Policy gates can be configured to fail a build automatically if a critical vulnerability is present, while lower-severity findings are reported asynchronously so they do not interrupt a fast-moving sprint.

The InfoSec principle that “security is everyone’s job” only becomes real when security tooling integrates into the workflow developers already use—version control, CI pipelines, and chat notifications—rather than requiring separate logins to separate portals.

Step 3: Build Good Citizens, Not Just Good Builds

The traditional deployment pipeline looks like this:

Design → Code → Integration Test → QA → InfoSec Review → Production

That model breaks down entirely when a DevOps team ships multiple times per day. There is no time for a manual InfoSec Review gate before every production push.

But consider what you are actually verifying at that gate. If security feedback has been automated and delivered to developers from the design phase onward, the InfoSec Review is not discovering new information—it is simply checking whether developers acted on what they already knew. That is a question of trust and process verification, not technical discovery.

When security is genuinely integrated earlier in the lifecycle, the pre-production InfoSec Review can be replaced with a lightweight, evidence-based checklist during change control:

• Have automated security scans (SAST and SCA) completed successfully at every stage of the pipeline for this application?
• Have developers been triaging and resolving critical and high-severity findings consistently throughout the sprint cycle?
• Does the most recent completed assessment meet the organisation’s defined security policy?

If the answer to all three is yes, deployment can proceed. The bottleneck gate is gone. The cultural shift here is significant: InfoSec moves from being a gatekeeper who approves releases to being the team that defines policy, provisions tools, and monitors compliance metrics—enabling developers rather than blocking them.

Putting It Together

Securing open-source-heavy DevOps pipelines does not require a separate security team running parallel reviews. It requires embedding security practices where development decisions are made:

1. Audit and approve your dependency inventory at design time, before significant code is written.
2. Automate SAST and SCA in your CI/CD pipeline so security feedback is continuous and does not require manual intervention.
3. Replace manual pre-production security gates with evidence-based policy checks that verify developers acted on the information they received throughout the cycle.

This is the operational definition of DevSecOps: security that is continuous, automated, developer-facing, and proportionate to the pace at which modern teams actually ship software.