What is Splunk Phantom Admin?

If you work on a security team, you probably deal with complex security infrastructure and a range of technologies from multiple vendors. You’re likely exposed to unquantified risk stemming from a constantly expanding attack surface and escalating volumes of threats. And like everyone else, you have limited resources to defend your entities. There’s is never enough budget or people. Fortunately, there is a better way. Force multiply your efforts with a Splunk phantom security orchestration, automation, and response platform.

Phantom integrates your team, processes, and tools together so you can bring your best defense forward. It allows you to automate repetitive tasks. So, you can focus on other mission-critical issues. It helps you up-level how you manage events and cases and improve collaboration and training across the team. Phantom allows you to triage every by executing routine workflows in seconds or minutes instead of hours or more if done manually. It helps you make your SOC operations repeatable and auditable. Teams are able to manage events and cases the same way, every time, regardless of the person working on them. You’re able to accurately measure and reduce risk. And finally, you can demonstrate measurable impacts on your key performance metrics like MTTR. See what phantom can do for you. Get started now with the phantom community edition.    

What are the benefits of Splunk Phantom Admin?

From the beginning, we have learned many sorts of Splunk platforms series like Splunk core developer, enterprise security, cloud admin, and IT service intelligence. This time we are meeting with Splunk phantom. Every software has its own performance, duties, and responsibilities with great impact then query raises that what about the software defensive security and how the software maintains their accuracy with higher scalability. With this overview, there are certain benefits related to Splunk phantom certified admin such as: 

• Create security for the management system
• Highly integration process
• Security work flowing system
• Automation security system
• Advanced threat catching actions

Why we should learn Splunk Phantom Admin?

Nowadays, this trending IT organization with the higher escalating tools. We are working with different technologies at a scale of interactions. If we are using the software too much then we need threat detection analysts and security system services. Then, a query comes as that why we should learn Splunk phantom and become certified. The most reason is that to automate and accelerate the process like shortage of IT security professionals, a large number of security point solutions, challenges of unstructured data, the investigative approach, people & processes, and workflow of threat mitigation timeline. And the admin facing common security operations challenges such as resources, products, alerts, static, speed, and costs. So, if you move along with Splunk phantom then you can evaluate delivering quality intelligence, improving the efficiency and efficacy of operations, enhancing incident response, and improves reporting and knowledge capture. As a Splunk phantom certified admin, you can make your software more planning, designing, creating, and debugging playbooks for phantom.

How our Splunk Phantom Admin course/training would help?

These tutorial courses will help you with the learning process of Splunk phantom admin with the SOAR platform (security orchestration, automation & response). This will make you more configure about the software system security. It solves security operations problems. Suppose, your software have limited resources, too many alerts, too many siloed products, lack of process, speed, & cost then it remediates with force multiply your team, clear 80% of alerts with no human interaction, unlock value from previous investments, establish SOPs (standard operating procedures) to be more effective, from 30 minutes to 40 seconds, & definitive ROI and hard cost savings. And it will give you work flowing ideology on security data, playbooks, actions, apps, assets, and owners.

After, getting the processed values then comes to the certification. If you get certified with the completion of training then it gives you competitive strength in automation and orchestration first, community focus, and enterprise architecture. This will make you more efficient in IT software infrastructure for ideal job opportunities with an expected high-paid salary. I wish that these statistics will give you more understanding of futuristic points of view.

Related video:

What the Gartner Magic Quadrant Measures

Gartner defines the Application Performance Monitoring (APM) market as software that facilitates application monitoring across three primary functional dimensions:

• Front-end monitoring – tracking the end-user experience in browsers and mobile apps.
• Application discovery, tracing, and diagnostics (ADTD) – mapping application topology, distributed tracing, and deep-dive diagnostics.
• Analytics – aggregating and interpreting performance data to surface actionable insights.

In the 2020 edition of the Magic Quadrant for APM, Gartner recognized four vendors as Leaders — the same four that led the 2019 report. The continuity signals that these platforms have built durable, mature capabilities that competitors in the Niche and Visionary quadrants are still working to replicate.

The four Leaders, listed below in alphabetical order, differ in market share, deployment model, and feature emphasis. Understanding those differences is essential when evaluating which tool fits your organization’s cloud strategy and engineering workflows.

1. AppDynamics

AppDynamics is a full-stack APM suite owned by Cisco. It is designed to improve digital experiences for both customers and internal users by giving engineering teams granular visibility into application behavior across multi-cloud environments.

Key capabilities

• Code-level visibility – traces performance down to individual lines of code and critical business transactions.
• End-user monitoring (EUM) – captures real-user data from browsers and mobile devices to correlate front-end experience with back-end performance.
• Infrastructure visibility – monitors servers, containers, and cloud resources alongside application metrics.
• Business performance monitoring – ties technical KPIs to business outcomes such as conversion rates and revenue impact.

AppDynamics is a common choice in large enterprise environments where aligning IT performance to business metrics is a priority.

2. Broadcom DX APM

Broadcom DX APM (formerly CA APM) is built on a microservices architecture, which gives it flexibility for large, distributed deployments. It is part of Broadcom’s broader DX Platform for IT operations management.

Key capabilities

• Microservices-based agent architecture – individual services can be scaled independently, avoiding the resource overhead of a monolithic agent.
• Cluster Management console – provides a single pane of glass for hardware utilization, service scaling, and onboarding new monitored projects.
• APM Command Center – enables centralized agent upgrades and configuration changes across the estate.
• Operational intelligence and app experience analytics – surfaces trends and anomalies across application and infrastructure layers.

DX APM tends to appeal to enterprises already invested in the Broadcom (formerly CA Technologies) ecosystem for IT operations and service management.

3. Dynatrace

Dynatrace markets itself as a Software Intelligence Platform rather than a point APM solution. It combines observability, AIOps, and digital experience management into a unified product.

Key capabilities

• Automatic discovery and instrumentation – Dynatrace’s OneAgent automatically discovers services, processes, and dependencies without requiring manual configuration.
• PurePath Technology – captures end-to-end distributed traces with timing and code-level context for every transaction across every tier, from the browser through to the database.
• Containerized microservices monitoring – detects and monitors microservices running in Kubernetes and other container orchestrators, and surfaces performance degradations automatically.
• AIOps (Davis AI engine) – correlates events and performs root-cause analysis automatically, reducing alert noise.
• Digital business analytics – connects user behavior data to conversion and revenue metrics.

Dynatrace is a popular choice for cloud-native and Kubernetes-heavy environments where automated observability at scale is a core requirement.

4. New Relic APM

New Relic APM is part of the broader New Relic observability platform. It is well regarded for its breadth of language support and the depth of transaction-level detail it surfaces for developers.

Key capabilities

• Auto-instrumentation for multiple languages – New Relic supports auto-instrumentation for a wide range of programming languages (including Java, .NET, Node.js, Python, Ruby, PHP, and Go as of the 2020 timeframe), reducing the setup burden on engineering teams.
• In-depth transaction tracing – shows exact method calls with line numbers and highlights external dependencies, making root-cause analysis faster.
• Full-stack visibility – provides a single view of the application, its infrastructure, and the end-user experience.
• Distributed tracing – follows requests across microservices and third-party services to surface latency bottlenecks.

New Relic’s pricing model and developer-friendly tooling make it a common choice for SaaS companies and teams that prioritize fast time-to-value.

Comparing the Four Leaders at a Glance

What to Consider When Evaluating APM Tools

Being named a Gartner Magic Quadrant Leader is a meaningful signal, but it should be the starting point of an evaluation rather than the conclusion. Consider the following factors when selecting an APM platform:

• Language and framework support – confirm that your primary runtimes are supported with auto-instrumentation, not just manual SDKs.
• Deployment model – SaaS, on-premises, and hybrid options vary by vendor; verify alignment with your data residency and compliance requirements.
• Pricing model – APM pricing can be per host, per user, per data ingested, or a combination. Model costs against your actual environment before committing.
• Integration ecosystem – check compatibility with your CI/CD pipelines, incident management tools (PagerDuty, Opsgenie), and cloud providers.
• Alerting and AIOps maturity – evaluate how well the platform reduces alert fatigue compared to a threshold-only approach.
• Time to value – agent-based auto-discovery dramatically reduces onboarding time; assess how quickly you can instrument a new service in your stack.

Running a proof-of-concept (PoC) against a representative sample of your production services remains the most reliable way to compare platforms before making a long-term commitment.

Source: Solutions Review coverage of the Gartner Magic Quadrant for Application Performance Monitoring, 2020.

Digital Channels Have Become the Storefront

When physical locations become inaccessible—whether due to public health restrictions, natural disasters, or shifting consumer habits—a company’s website or mobile application instantly becomes its primary customer-facing interface. Organizations that had not yet invested seriously in digital transformation find themselves accelerating years of planned work into weeks. IT teams are no longer supporting the business; in many cases, they are the business.

Luke Rogers, General Manager for Canada at AppDynamics (a Cisco company), described the situation clearly: organizations that previously treated IT as a back-office function are arriving at APM vendors saying, “Our IT has become our business, and our applications have become the storefront, and our ability to deliver a digital experience is everything.”

The Real Cost of Poor Application Performance

Application performance directly affects revenue, customer retention, and brand trust. The relationship between page load time and user abandonment is well established in web performance research. As a reference point cited in the original reporting: when mobile page load time increases from one second to ten seconds, the probability of a visitor leaving the page before it loads increases by approximately 123 per cent.

This figure underlines a critical question every engineering team should be asking: Does your infrastructure scale to handle demand spikes without degrading the user experience? A site that performs well under normal load may buckle when traffic doubles or triples, precisely when uptime matters most.

What Application Performance Monitoring (APM) Actually Does

APM tools give engineering and operations teams a comprehensive, real-time view of how their applications behave in production. Rather than waiting for a customer complaint or an on-call alert, APM surfaces problems—slow database queries, memory leaks, failing third-party API calls, saturated connection pools—before they cascade into outages.

Key capabilities that mature APM platforms provide include:

• Full-stack visibility: Traces a single user transaction from the browser or mobile client through load balancers, application servers, microservices, databases, and external APIs.
• Baseline and anomaly detection: Establishes normal performance thresholds and alerts when metrics deviate, reducing alert fatigue compared to static threshold monitoring.
• Distributed tracing: Correlates spans across service boundaries, which is essential in microservice and Kubernetes-based architectures.
• Infrastructure correlation: Links application-layer symptoms (slow response times) to infrastructure-layer causes (CPU contention, disk I/O, network latency).
• Business transaction monitoring: Tags traces by business context—checkout flow, login, search—so engineers can prioritise fixes by revenue impact rather than raw error count.

Full-Stack Visibility: Why Partial Visibility Is Dangerous

A common failure mode is monitoring only one layer of the stack. Teams may have excellent application-level metrics but no visibility into the underlying compute and storage, or they may monitor infrastructure health without correlating it to user-facing latency. When an incident occurs, engineers spend the majority of their time on root-cause analysis rather than remediation because they lack a unified picture.

Rogers summarised the requirement well: “You need something that provides that full-stack visibility of everything—from the compute storage system layer all the way up to the application layer. There’s real value in that.”

In practice, full-stack observability typically means integrating three signal types:

1. Metrics — time-series data on CPU, memory, request rates, error rates, and latency percentiles.
2. Logs — structured or semi-structured event records from applications and infrastructure components.
3. Traces — end-to-end records of individual requests flowing through distributed systems.

Questions Engineering Teams Should Answer Before a Traffic Spike

Rather than discovering scaling limits under fire, DevOps and SRE teams can conduct structured readiness reviews. The following questions provide a practical starting framework:

• What is the current baseline throughput (requests per second) for each critical user journey?
• At what point does response time exceed your SLO threshold—and have you load-tested to that ceiling?
• Are auto-scaling policies configured and validated for your application tier, database tier, and CDN?
• Do you have circuit breakers and graceful degradation in place for third-party dependencies?
• Is your on-call runbook up to date, and do engineers have the access they need to act quickly during an incident?
• Are data residency and compliance requirements (data sovereignty) documented for all services that process customer data?

The APM Market and Vendor Landscape

Application performance management has grown into a substantial and competitive market. According to IDC figures cited in the original reporting, the APM market grew from approximately $4.3 billion in 2018 to $5.8 billion in 2019, with continued growth expected. Several enterprise and open-source options exist, including AppDynamics (Cisco), Dynatrace, New Relic, Datadog, Elastic Observability, and open-source stacks built around Prometheus, Grafana, Jaeger, and OpenTelemetry.

When evaluating APM tooling, consider the following criteria:

• Language and framework support: Does the agent or SDK support your primary tech stack without requiring significant instrumentation effort?
• Cloud and container native support: Does the platform have first-class support for Kubernetes, serverless functions, and managed cloud services?
• Data retention and pricing model: Ingestion-based pricing can become expensive at scale; understand the cost model before committing.
• Data residency options: For regulated industries or specific jurisdictions, confirm where telemetry data is stored and processed.
• Integration breadth: Alerting, incident management (PagerDuty, Opsgenie), CI/CD pipelines, and ITSM platforms should integrate without heavy custom work.

Key Takeaways for DevOps and SRE Teams

Application health is no longer solely an engineering concern—it has direct and measurable business impact. Engineering leaders who can present performance data in business terms (conversion rate, revenue per transaction, customer churn risk) are far more likely to secure the investment needed to address underlying infrastructure and observability gaps.

• Establish SLOs for every customer-facing service and measure them continuously, not just during incidents.
• Invest in full-stack observability before a demand spike, not after one exposes your blind spots.
• Treat load testing as a regular engineering practice, not a one-time pre-launch activity.
• Document data sovereignty requirements early, especially if operating across multiple jurisdictions or in regulated sectors.
• Use observability data to build the business case for infrastructure investment with leadership.

The organisations that weather unexpected demand surges best are those that have invested in visibility, automation, and resilience engineering before the surge arrives.

APM Adoption Is Nearly Universal—But Mostly Reactive

Application performance management (APM) tools are now a standard part of the IT toolbox: nine out of ten IT professionals surveyed reported using APM tools deployed either on-premises or via a SaaS platform. Despite that near-universal adoption, the SolarWinds survey reveals a significant gap between how teams could use APM and how they actually do.

Nearly eight in ten respondents said they spend less than 10% of their time proactively optimizing their environments. In practice, this means most APM usage is driven by incidents—teams reach for monitoring data after something breaks rather than using continuous telemetry to prevent issues before they surface.

The Three Biggest APM Challenges

Survey respondents identified a clear hierarchy of obstacles blocking more effective APM use:

• Lack of personnel training (57%) — The top barrier by a wide margin. Teams have the tools but not always the skills to extract full value from them.
• Lack of awareness of available APM solutions (44%) — Many practitioners don’t have a clear picture of what the current APM market offers, making tool selection difficult.
• Confusion over which solution fits their needs (42%) — Even when teams are aware of their options, choosing the right tool for their specific architecture and scale remains a challenge.

Jim Hansen, Vice President of Products for Application Management at SolarWinds, attributed part of the problem to tool sprawl. Organizations are simultaneously running database monitoring (64%), application monitoring (63%), and infrastructure monitoring (61%)—three overlapping disciplines that each demand expertise and context-switching.

Mixed Architectures Are Complicating APM Coverage

The challenge is compounded by the fact that most organizations are not running a single, consistent application architecture. Survey respondents reported managing APM across:

• Monolithic (traditional on-premises) architectures — 59% of respondents
• N-tier service-oriented architectures (SOA) — 40%
• Microservices-based applications — 39%

Running APM across all three simultaneously means teams need tools—and skills—suited to fundamentally different observability models. A single agent-based APM product may work well for a monolithic app but provide incomplete coverage for a distributed microservices environment where requests span dozens of services and multiple infrastructure layers.

Why Microservices Will Force a More Proactive Approach

Hansen pointed out that as organizations migrate away from monolithic applications, the shortcomings of a reactive APM posture will become harder to ignore. Microservices architectures introduce dense dependency graphs: a single user-facing transaction may traverse ten or more services, each with its own latency profile, failure modes, and infrastructure dependencies.

In that context, waiting for an alert before investigating is often too late—by the time a symptom surfaces, root cause may already be buried several hops upstream. This is driving interest in APM platforms that correlate signals across application traces, infrastructure metrics, and logs in a unified view, and that can surface anomalies automatically rather than requiring manual threshold tuning.

Confidence Is High—But Skill Gaps Remain

Despite the challenges, respondents expressed a relatively high degree of self-assessed confidence: 84% said they were confident in their ability to manage application and infrastructure performance. However, the specific use cases where respondents felt most comfortable reveal a continued reactive bias—40% said they are most comfortable using APM tools to troubleshoot application performance issues after they arise.

The top three insights teams said they gain from APM tools were:

1. Preventing application outages — 73%
2. Preventing app slowdowns related to performance or capacity — 63%
3. Improving user and customer experience — 62%

These three outcomes all point toward proactive APM value—yet the earlier finding that most time is spent reactively suggests a gap between aspiration and practice. Teams know what good looks like; they’re not consistently getting there.

Where Teams Want to Improve

When asked about priority areas for development, respondents gave a tightly clustered set of answers:

• Improving ability to track impact across key business metrics — 31%
• Improving ability to troubleshoot application issues — 30%
• Improving application code performance — 29%
• Managing and improving end-user performance — 29%

The clustering of these responses suggests teams recognize they need broad improvement across the APM skill set—not a single weak area—which reinforces the training-gap finding at the top of the challenges list.

What This Means for DevOps Teams

Reactive APM is better than no APM, but it falls short of the continuous feedback loops that underpin mature DevOps and SRE practices. Proactive APM—using baseline telemetry, anomaly detection, and dependency mapping to identify degradation before users notice—requires both the right tooling and teams that are trained to act on the signals those tools surface.

Several practical steps can help bridge the gap identified in this survey:

• Consolidate monitoring disciplines. Where possible, use platforms that unify application, infrastructure, and database observability to reduce context-switching and correlation overhead.
• Define SLOs and alert on symptoms, not just thresholds. Error-budget-based alerting encourages teams to act before outages rather than after them.
• Invest in training alongside tooling. The survey’s top finding—57% citing lack of training—suggests that tool procurement outpaces enablement. Structured onboarding and regular practice with APM dashboards and query languages pays dividends.
• Audit tool sprawl. Running three parallel monitoring categories without integration creates noise and skill fragmentation. Evaluate whether consolidation or tighter integration is feasible.
• Align APM to business outcomes. The 31% of respondents who want better business-metric correlation are pointing toward a real best practice: tying telemetry to revenue, conversion, or reliability SLAs makes APM relevant beyond the ops team.

As hybrid architectures and microservices continue to proliferate, the organizations that invest now in proactive APM practices—and the training to back them up—will be better positioned to maintain reliability at scale without proportional growth in toil.

Data Theorem, Inc. a provider of modern application security, introduced a new SPA security service that delivers automated discovery and continuous dynamic runtime vulnerability inspection of modern web single-page applications (SPAs). Purpose-built for SPAs, the new service is differentiated in its runtime security analysis that supports both GraphQL and REST API services, the popular services for SPAs that dynamically deliver a faster and richer web user experience.

With DevOps teams rapidly building web SPAs, security and IT teams using traditional web app scanners lack the ability to gather application insights and inspect for security vulnerabilities on these new modern web apps. Now with this new offering from Data Theorem, for the first time users can fully discover and inspect vulnerabilities with dynamic runtime analysis for both GraphQL and REST API services.

Tech News: Dremio Releases Data Lake Engines for AWS, Azure, and Hybrid Cloud; Enhances Performance on Data Lake Storage

“SPA security is the new frontier for modern web application security, and like mobile it is tightly coupled to the explosion and growth of GraphQL and API backend services,” said Doug Cahill, senior analyst and group practice director of cybersecurity for ESG. “To best protect these services from attack, organizations need a solution that delivers both continuous security vulnerability inspection and runtime analysis that supports both GraphQL and REST API services.”

Businesses today delivering modern web applications build SPAs to deliver a richer and faster user experience that is similar to what they deliver with their mobile apps. Similar to mobile app protection, traditional web app scanners lack the ability to add security insights to SPAs because of the dynamic nature of the SPA JavaScript architecture. In addition, GraphQL adds a new attack surface due to the enhanced flexibility it provides, making it difficult to protect against malicious queries. These attack queries could lead to denial of service attacks, or unauthorized access to private data.

“Growth of SPA deployment and usage increases every year because organizations want their web experience to be as good as their mobile app experience,” said Doug Dooley, Data Theorem COO. “But security tools have not kept up with this modern software development trend. With our first web app security offering launching today, Data Theorem is leaping ahead of the competitive landscape to now serve users’ complex security needs beyond API and mobile. We were already leading in runtime analysis for mobile apps, and now we offer similar depth of runtime analysis to protect these popular SPAs.”

Tech News: Oracle Expands its Security Portfolio with New Cloud Services; Unveils New Innovations for its Data Management Portfolio

Today’s SPA security solution is offered as a component of Data Theorem’s API Discover and API Inspect, which together address security concerns such as Shadow APIs, Serverless Applications, and API Gateway cross-check validation by conducting continuous security assessments on API authentication, authorization, encryption, availability, serverless functions, and policy compliance.

The API security solutions support Amazon Web Services, Google Cloud, and Microsoft Azure to discover modern APIs and to enumerate the specification using standards such as Swagger and Open API 3.0.