How to Compare Cloud Security Tools: The Evaluation Framework 

Key Insights

• Why do feature checklists fail for cloud security evaluation? Every CNAPP and CDR vendor checks the same boxes—vulnerability scanning, runtime detection, compliance. The checklists are identical because vendors design marketing to match them, not because capabilities are equivalent. A mid-sized fintech evaluated five platforms using standard criteria; every vendor scored equally. Three months later, their team needed four dashboards and 11 hours to trace a single credential theft.
• What is the Full Attack Story Framework? Five evaluation criteria that reveal how tools perform during actual incidents: Layer Coverage (cloud, Kubernetes, container, application visibility), Correlation Capability (connecting events into one timeline), Context Quality (runtime behavior vs. static configs), Response Options (graduated containment actions), and Investigation Time (alert to root cause speed). These criteria separate tools that generate noise from tools that enable confident response.
• What’s the difference between CNAPP and CADR for incident response? CNAPP platforms are wide but shallow—built for compliance teams, not incident responders. They answer “what could be wrong?” not “what’s happening now?” CADR (Cloud Application Detection & Response) combines ADR, CDR, KDR, and EDR into unified attack story generation. When teams try to use CNAPP for incident response, they typically end up exporting to a SIEM and correlating manually anyway.
• How do I reduce cloud incident investigation time? Tools that generate attack narratives automatically—rather than presenting disconnected alerts—reduce investigation time by up to 90%. The key is eliminating manual correlation across dashboards. Benchmark: mature security teams should trace attack entry to impact in under 15 minutes. If your tooling requires 2+ hours, investigation time is a liability.
• How does ARMO CADR compare? ARMO is the first platform to unify application, cloud, Kubernetes, and endpoint detection into a single correlation engine. Using eBPF-powered sensors (1-2.5% CPU, 1% memory overhead) and Application Profile DNA behavioral baselines, ARMO builds LLM-powered attack stories that reduce investigation from days to minutes. Built on Kubescape, trusted by 50,000+ organizations.

Introduction

You’re not struggling to find cloud security tools. You’re struggling to compare them meaningfully.

Every vendor claims “comprehensive coverage” and “real-time detection.” Their feature matrices look identical. Their demos all show impressive dashboards catching simulated attacks. But when an actual attacker is moving through your Kubernetes environment—stealing credentials, pivoting across namespaces, reaching cloud APIs—the difference between tools becomes painfully clear.

We’ve watched security teams during real incident. We’ve seen a Fortune 500 SOC spend 14 hours correlating alerts across four tools while attackers exfiltrated customer data. We’ve seen 3,400 CVE alerts turn into 47 actionable vulnerabilities after runtime reachability analysis. We’ve watched teams discover, post-breach, that their tool saw every piece of the attack—in five different dashboards, none of which connected the dots.

The pattern is consistent: feature checklists fail. Tools that look identical on paper perform dramatically different when you need to understand an attack chain that spans your cloud infrastructure, Kubernetes control plane, container runtime, and application code.

This guide introduces the Full Attack Story Framework—five evaluation criteria that reveal how tools actually perform during incidents, not just what features they list. These criteria separate tools that help you understand what happened from tools that just add to the noise.

Why Traditional Comparison Methods Fail

Before diving into what works, let’s examine why the standard approach to tool evaluation consistently leads teams to the wrong choice.

The Feature Checklist Problem

Pull up any CNAPP, CDR, or cloud security vendor’s website. They all check the same boxes: vulnerability scanning ✓, runtime detection ✓, compliance reporting ✓, Kubernetes support ✓. The checklists are functionally identical because vendors design their marketing to match the checklist, not because their capabilities are equivalent.

Here’s what this looks like in practice: A mid-sized fintech company evaluated five cloud security platforms last year using a standard 40-point feature checklist. Every vendor scored between 37 and 40. The team chose the highest-rated option. Three months later, during an actual credential theft incident, their SOC needed four dashboards, two CLI tools, and 11 hours to understand what happened. The tool detected the attack—it just couldn’t tell them the story.

When everything checks every box, the checklist tells you nothing about what matters: how the tool performs when you need it most.

The “More Tools” Fallacy

Here’s an uncomfortable truth: teams running multiple specialized security tools often have worse visibility than those with fewer, well-integrated solutions.

Think about what happens when you add EDR + CSPM + CDR + a vulnerability scanner + a SIEM. Each tool generates alerts in its own format, displays data in its own dashboard, uses its own terminology. Your analysts become the glue, manually correlating signals across four or five consoles while the attacker continues their work.

We’ve seen this turn a 30-minute containment window into a 3-day investigation. By the time the team understood the attack chain, the attacker had established persistence in two additional clusters. The breach notification went from “contained incident” to “material event requiring disclosure.” 

The cost wasn’t just the breach—it was the reputational damage of announcing something that could have been stopped.

The question isn’t which tool has more features. It’s which tool helps you understand what happened, why it matters, and how to stop it without breaking production.

The Demo vs. Reality Gap

Vendor demos are theater. They show cherry-picked attack scenarios in controlled environments with perfectly tuned detection rules. The attacker conveniently triggers exactly the signatures the tool is calibrated for. The investigation clicks through exactly three screens to a satisfying conclusion.

Real incidents don’t work that way. Real attacks chain across layers in ways demos never show. An attacker exploits a deserialization vulnerability in your payment service, escapes the container, reads Kubernetes secrets, and uses those credentials to query your cloud APIs. The attack spans four layers in eight minutes. Tools that look impressive detecting a single-layer attack often fail when you need to trace an attack that spans your entire stack—because they were built to detect, not to correlate.

The wrong choice here isn’t just wasted budget. It’s your team drowning in alerts during an actual incident, unable to answer the CEO’s question: “How bad is this?” It’s discovering after a breach that your tool saw every piece of the attack—in five different dashboards, none of which connected the dots.

That’s why we developed the Full Attack Story Framework—to evaluate tools based on how they perform when attacks cross boundaries, not how they look in scripted demonstrations.

What a Full Attack Story Actually Looks Like

Before diving into the framework criteria, let’s look at a concrete attack scenario—the kind of multi-layer attack that separates effective tools from noise generators.

At 2:47 AM, an attacker exploits a deserialization vulnerability in a payment processing service running in your production Kubernetes cluster. The vulnerable Java application deserializes untrusted data, allowing the attacker to execute arbitrary code.

Stage 1 (Application Layer): The exploit triggers. A malicious payload executes within the application context. Time elapsed: 0 seconds.

Stage 2 (Container Layer): The compromised application spawns a reverse shell—a bash process that shouldn’t exist in a Java application container. The attacker now has interactive access. Time elapsed: 12 seconds.

Stage 3 (Kubernetes Layer): The attacker reads the service account token automatically mounted at /var/run/secrets/kubernetes.io/serviceaccount. They query the Kubernetes API server: what secrets exist in this namespace? The overly permissive RBAC lets them read database credentials. Time elapsed: 45 seconds.

Stage 4 (Cloud Layer): Using the database credentials, the attacker connects to RDS and begins querying transaction records. They identify high-value data and start exfiltration. Time elapsed: 90 seconds.

Total time from initial exploit to data access: under two minutes.

How different tools handle this:

A tool with full attack story capability shows this as one incident with four connected stages. Each stage includes the specific process, API call, or query that proves what happened. Your analyst sees the complete chain in one view: application exploit → container compromise → Kubernetes credential theft → cloud data access. Investigation time: 8 minutes to understand scope and impact.

A tool without correlation shows: (1) a medium-severity “suspicious process” alert from container monitoring, (2) a low-severity “API query” log entry from Kubernetes audit logs, (3) a “new database connection” notification from cloud monitoring. Three alerts, three priorities, three dashboards, no obvious connection. By the time an analyst correlates them manually—if they correlate them at all—the attacker has been in the database for hours. Investigation time: 6-14 hours, if the connection is made at all.

This scenario isn’t hypothetical. It’s the pattern we see repeatedly in cloud-native breaches. The question for your tool evaluation: which experience will your team have?

The Full Attack Story Framework: 5 Criteria That Actually Matter

This framework evaluates cloud security tools based on a single question: can this tool help me understand a complete attack story, or will it hand me fragments I have to assemble myself?

These five criteria reveal the difference between tools that generate noise and tools that enable fast, confident response. For each criterion, we include the specific question you should ask vendors during demos—questions designed to expose capability gaps that feature checklists miss.

Criterion 1: Layer Coverage

What layers can the tool actually see?

Modern cloud attacks don’t stay in one layer. The scenario above illustrates this: application exploitation leads to container compromise leads to Kubernetes credential theft leads to cloud data access. A tool that only sees cloud API calls misses the application exploitation. A tool that only sees container behavior misses the cloud pivot. Partial visibility means partial stories.

Effective tools provide visibility across:

• Cloud APIs and control plane activity (IAM changes, resource creation, API calls across AWS, Azure, GCP)
• Kubernetes control plane (RBAC modifications, pod deployments, secret access, config changes)
• Container runtime (process execution, network connections, file access, syscalls)
• Application layer (function calls, code execution paths, API requests, data access patterns)

The vendor challenge: “Show me an attack that started at the application layer and moved to cloud infrastructure—like the Log4j scenario where application exploitation led to cloud credential theft. Can I see the full chain in one view, or do I need to check multiple dashboards?”

If they need to switch dashboards or say “we integrate with” another tool for that visibility, they’ve failed the test. Integration isn’t correlation.

Criterion 2: Correlation Capability

Can the tool connect events across layers into a single incident timeline?

Seeing each layer isn’t enough. If your tool shows you three alerts from three layers in three different places, you still have to do the correlation work yourself. Three separate investigations instead of one response. Your analysts become the glue.

True correlation means the tool understands that the suspicious process in the container, the unusual Kubernetes API call, and the cloud database connection are all part of the same attack—and shows them as a connected story with evidence linking each stage.

The vendor challenge: “If an attacker exploits a vulnerability, escalates privileges through Kubernetes RBAC, and accesses cloud resources—will I see one incident timeline or twelve separate alerts? Show me the linking evidence between stages.”

Criterion 3: Context Quality (Runtime vs. Static)

Does the tool show actual runtime behavior or just static configurations?

This is the difference between knowing a door could be unlocked and watching someone walk through it.

Consider two containers with the same critical CVE. Container A runs an internal batch job with no network access, no sensitive data, no external exposure. Container B runs your payment API, handles PCI data, and is accessible from the internet. Static scanners flag both as “critical.” Runtime-aware tools understand that Container B is an active threat requiring immediate action, while Container A can wait for the next maintenance window.

This is why runtime reachability analysis matters. Teams using runtime context typically reduce actionable CVEs by 80-90%—not by ignoring vulnerabilities, but by understanding which ones attackers can actually exploit in their specific environment.

Tools that build behavioral baselines—understanding what “normal” looks like for each workload—can detect anomalies that signature-based detection misses. When a database container suddenly spawns a shell process, that’s a deviation worth investigating regardless of whether it matches a known attack signature.

The vendor challenge: “How do you determine which vulnerabilities are actually exploitable in my environment? Show me how you distinguish a theoretical CVE from a runtime-reachable one. What’s your process for building behavioral baselines?”

Criterion 4: Response Options

What actions can you take directly from the tool?

Detection without response capability means more handoffs, more delay, more damage. Every minute you spend switching consoles, filing tickets, or waiting for another team is a minute the attacker uses to move laterally, establish persistence, and reach their objective.

But not all response options are equal. The ability to kill a process is useful, but what if you need forensic evidence? What if killing the container triggers an autoscaling event that creates a fresh target? Graduated response options matter:

• Can you soft-quarantine a container (block network but preserve for forensics) without killing it?
• Can you block specific network paths to prevent lateral movement while maintaining service?
• Can you revoke or rotate compromised credentials directly from the investigation view?
• Are there automated playbooks for common scenarios like cryptomining or credential theft?

The vendor challenge: “Walk me through your response options. If I detect a compromised container, can I soft-quarantine it for forensic investigation, or is my only option to kill it? What happens to the evidence?”

Criterion 5: Investigation Time

How long from initial alert to understanding root cause and blast radius?

If investigation takes days, attackers have days to move laterally, establish persistence, and exfiltrate data. Speed matters, but speed without accuracy creates false positives that waste even more time and erode trust in the tool.

Benchmark for mature security teams: investigation should take under 15 minutes for common attack patterns. If your current tooling requires more than 2 hours to trace an attack from entry to impact, your investigation time is a liability—it’s time the attacker uses and you don’t have.

The best tools generate attack narratives automatically. Instead of your analysts digging through logs across multiple systems to piece together what happened, they see the complete attack story: initial access, persistence mechanisms, lateral movement, and potential impact—mapped to frameworks like MITRE ATT&CK with evidence at each stage.

The vendor challenge: “Walk me through an investigation from alert to root cause. How many dashboards do I need to check? How many queries do I need to run? Time it. Show me real investigation, not just detection.”

Applying the Framework: How Tool Categories Compare

Different tool categories were built for different purposes. Understanding where each excels—and where each falls short against the Full Attack Story Framework—helps you evaluate specific vendors strategically, not just check boxes.

CSPM Tools (Cloud Security Posture Management)

Strong at: Configuration scanning, compliance benchmarks, infrastructure visibility, audit evidence collection

Weak at: Runtime behavior, application-layer visibility, attack correlation, incident response

Framework assessment: CSPM tools operate on snapshots, not live events. They tell you what’s misconfigured, not what’s actively being exploited. Layer coverage is limited to cloud infrastructure—they can’t see inside containers or applications. No correlation across runtime layers. Static context only, which means every misconfiguration looks equally urgent regardless of whether it’s exploitable.

Verdict: CSPM creates posture reports, not attack stories. These tools answer “what could be wrong?” not “what’s happening now?” Useful for compliance evidence and security hygiene, but insufficient for incident response. When teams try to use CSPM during active incidents, they end up exporting data to a SIEM and correlating manually anyway.

Traditional EDR Extended to Cloud

Strong at: Endpoint visibility, established detection patterns, mature response playbooks, analyst familiarity

Weak at: Kubernetes context, container ephemeral nature, cloud API visibility, cloud-native attack patterns

Framework assessment: EDR tools designed for persistent servers struggle fundamentally with containers that live for seconds. By the time a scheduled scan runs, the container may no longer exist. Missing Kubernetes layer entirely—EDR agents don’t understand namespaces, RBAC, or service accounts. Alerts lack cloud context, so analysts can’t understand how endpoint compromise connects to cloud resource access.

Verdict: EDR heritage doesn’t translate to cloud-native environments. The attack surface is different. The speed of change is different. The context needed for investigation is different. Purpose-built tools outperform adapted legacy tools.

Container Security Platforms

Strong at: Container runtime monitoring, image scanning, Kubernetes-native integration, workload protection

Weak at: Full-stack correlation, application code visibility, cloud API context, cross-cluster attack tracking

Framework assessment: Good container and Kubernetes layer coverage—these tools understand pods, namespaces, and container behavior. Limited visibility into application-layer attacks (SQL injection, SSRF, deserialization) and cloud control plane activity. Correlation typically stops at the container boundary, so attacks that pivot to cloud resources appear as separate, lower-priority events.

Verdict: Strong for container-specific threats, but attack stories that span layers remain fragmented. Good as a component, insufficient as a complete solution for modern attack patterns.

CNAPP Platforms

Strong at: Broad coverage claims, agentless scanning options, compliance consolidation, single-pane-of-glass marketing

Weak at: Runtime depth, incident response workflows, attack story correlation, speed of detection

Framework assessment: Wide but shallow. Many CNAPP platforms are assembled from acquisitions without true integration—they bolt together CSPM, CWPP, and vulnerability scanning under one console without correlating the data. Layer coverage may exist on paper, but check whether posture findings actually connect to runtime detections or just appear in separate tabs.

Verdict: CNAPP platforms are built for compliance teams and security posture reporting, not for SOC teams responding to active threats. A unified console doesn’t mean correlated data. Ask specifically: “If your CSPM component finds a misconfiguration and your runtime component detects an exploit of that misconfiguration, will they appear as one incident?” The answer is usually no.

Cloud Application Detection & Response (CADR)

This category was built specifically for the challenges the Full Attack Story Framework addresses: connecting signals across the entire cloud stack to understand complete attack chains and respond before attackers achieve their objectives.

CADR platforms like ARMO CADR combine what other tools treat separately: ADR (application-layer detection and response), CDR (cloud infrastructure detection), KDR (Kubernetes detection), and EDR (endpoint/container detection) into unified correlation. Instead of presenting twelve alerts from four layers, they construct the attack timeline automatically with evidence linking each stage.

ARMO’s specific approach uses eBPF-powered sensors at the kernel level—overhead of just 1-2.5% CPU and 1% memory—to monitor runtime behavior without impacting application performance. Each workload gets an Application Profile DNA: a behavioral baseline that defines what “normal” looks like for that specific container, enabling anomaly detection that signature-based tools miss.

The result: LLM-powered attack story generation that reduces investigation time by 90%. Instead of days correlating logs across dashboards, analysts see the complete chain—from initial access through lateral movement to impact—with specific evidence at each step. The attack scenario we described earlier? ARMO shows it as one incident in one view, not three alerts in three dashboards.

Framework assessment: Full layer coverage (cloud, Kubernetes, container, application). True cross-layer correlation with evidence graphs. Runtime context through behavioral baselines. Graduated response options (Kill, Stop, Pause, Soft Quarantine). Attack story generation that compresses investigation from days to minutes.

This category represents the framework’s criteria implemented as a product architecture. But capabilities vary by vendor—evaluate specific implementations against the five criteria, not just the category label.

Tool Category Comparison: Full Attack Story Framework Assessment

The table below summarizes how each tool category performs against the five framework criteria. Use this as a quick reference during your evaluation process.

Tool Category	Layer Coverage	Correlation	Context Quality	Response Options	Investigation Time
CSPM	Cloud only	None	Static only	Alert only	N/A (no runtime)
EDR Extended	Endpoint + limited cloud	Minimal	Runtime (endpoints)	Strong (endpoints)	Hours (manual correlation)
Container Security	Container + K8s	Within container layer	Runtime (containers)	Moderate	Hours (cross-layer gaps)
CNAPP	Broad but shallow	Limited (siloed modules)	Mostly static	Varies widely	Hours-days (fragmented)
CADR (ARMO)	Full stack (cloud, K8s, container, app)	Full cross-layer correlation	Runtime + behavioral baselines	Graduated (Kill, Stop, Pause, Quarantine)	Minutes (90% reduction)

Note: Individual vendors within each category may vary. This table represents typical category characteristics—always evaluate specific implementations against the framework criteria during demos and POCs.

The Evaluation Checklist: What to Do During Demos and POCs

Use this checklist to apply the Full Attack Story Framework during your actual evaluation process. These aren’t theoretical considerations—they’re the specific questions and tests that reveal capability gaps feature matrices hide.

During Vendor Demos

• Request a multi-layer attack simulation—not just endpoint OR cloud, but attacks that cross boundaries like the scenario described above. Watch how they handle it.
• Count the dashboards needed to trace an attack from entry point to impact. More than one view is a red flag.
• Ask to see actual investigation workflow, not just detection alerts. Detection is easy; correlation is hard.
• Test response actions—can you isolate a container without killing the entire pod? Can you preserve forensic evidence?
• Time the investigation. From alert to root cause understanding, how many minutes? If the demo doesn’t include this, ask why.
• Ask all five vendor challenge questions from the framework above.

During POC

• Measure time from alert to root cause understanding—not just detection time. Detection without context is noise.
• Evaluate alert quality vs. quantity. Is the tool high-fidelity or a noise factory? How many alerts can your team realistically investigate?
• Test with your real workloads in your actual environment, not vendor-provided scenarios designed to show the tool at its best.
• Measure performance impact (CPU, memory overhead) on monitored workloads. Overhead above 3% is a concern; above 5% is a problem.
• Run a simulated multi-layer attack and have your team investigate it. Can they actually use this tool under pressure?
• Check integration with your existing workflow: SIEM, SOAR, ticketing. Friction during incidents is dangerous.

Red Flags That Should Disqualify Vendors

• “Comprehensive coverage” without specific layer breakdown—this usually means shallow coverage of everything
• Separate dashboards for different attack stages—you shouldn’t need to manually correlate during incidents
• “We integrate with” instead of “we correlate across”—integration means data exists somewhere; correlation means it’s connected
• Response options limited to “alert and escalate”—if you can’t take action from the tool, you’re adding delay
• Demos that only show single-layer attack scenarios—either they can’t handle multi-layer attacks or they’re hiding limitations
• Inability to show behavioral baselines for workloads—without baselines, everything is equally suspicious
• Vague answers about investigation time—”it depends” usually means “too long”

Kubernetes Environments Require Different Evaluation Criteria

If you’re running Kubernetes at scale, generic cloud security evaluation criteria miss critical requirements. Kubernetes introduces attack surfaces and operational patterns that traditional tools weren’t designed to handle—and that adapted legacy tools handle poorly.

Kubernetes-specific evaluation points:

RBAC Complexity: Kubernetes RBAC isn’t just “who can access what”—it’s role bindings, cluster roles, service accounts, and namespace-scoped permissions that interact in ways creating privilege escalation paths invisible to cloud-only tools. Can your tool detect that a developer’s role binding combined with a default service account creates cluster-admin equivalent access? Can it trace an attacker’s use of an overly permissive service account token? Tools built for IAM don’t understand this attack surface.

Ephemeral Workloads: Containers live for seconds or minutes. A pod handling a single request might exist for 30 seconds. Tools designed for persistent servers miss short-lived containers entirely—by the time a scheduled scan runs, the compromised container no longer exists, and neither does the evidence. Ask how the tool handles workloads that terminate before scheduled scans run.

Multi-Cluster Visibility: Real Kubernetes deployments span multiple clusters across environments—production and staging, different regions, different cloud providers. Attackers who compromise one cluster often pivot to others. Can the tool correlate attacks that move between clusters? Or does each cluster appear as an isolated environment?

Control Plane Monitoring: Attacks on the Kubernetes API server, etcd, and scheduler are different from workload attacks. An attacker who can modify etcd directly doesn’t need to compromise individual containers. Does the tool monitor the control plane or just workloads?

For teams evaluating Kubernetes security platforms, start with tools built specifically for cloud-native environments rather than adapted from legacy architectures. The most effective tools—like those built on Kubescape, trusted by over 50,000 organizations with 11K+ GitHub stars—understand Kubernetes natively because they were designed for it from the start, not retrofitted after the fact.

Conclusion: From Siloed Alerts to Full Attack Visibility

Traditional tool comparisons optimize for the wrong outcomes. Feature checklists tell you what vendors claim to do. Marketing matrices show what boxes they’ve checked. The Full Attack Story Framework tells you what will actually happen when an attacker chains across your cloud, Kubernetes, container, and application layers—which is what attackers actually do.

The five criteria—Layer Coverage, Correlation Capability, Context Quality, Response Options, and Investigation Time—separate tools that generate noise from tools that enable fast, confident response. Apply them during your evaluation. Ask the vendor challenge questions. Time the investigations. Test with real attacks, not scripted demos.

When evaluating cloud security tools, don’t ask “which tool has more features?” Ask “which tool will help my team understand a complete attack story and respond before the attacker achieves their objective?”

Tools built for this problem—like ARMO CADR—exist because siloed alerts aren’t a security solution; they’re just organized noise. Full-stack correlation, behavioral baselines, and attack story generation transform incident response from days of log correlation to minutes of contextual understanding.

Ready to see how the Full Attack Story Framework applies to your environment? Request a demo of ARMO CADR and watch how attack stories are built across your cloud stack—the multi-layer scenario we described, traced in one view instead of four dashboards. 

Frequently Asked Questions

What is CADR (Cloud Application Detection and Response)?

CADR is a security category that combines application-layer detection (ADR), cloud infrastructure detection (CDR), Kubernetes detection (KDR), and endpoint/container detection (EDR) into a unified platform. Unlike tools that monitor individual layers in isolation, CADR correlates signals across the entire cloud stack to build complete attack stories. This enables security teams to understand multi-layer attacks as single incidents rather than disconnected alerts requiring manual correlation.

How do I evaluate cloud security tools if I don’t have a dedicated security team?

Focus on tools that reduce investigation complexity rather than adding capabilities. If you don’t have dedicated security analysts, you need tools that provide context automatically—attack stories, not raw alerts. The Full Attack Story Framework still applies: prioritize correlation capability and investigation time over feature breadth. A tool that surfaces one high-confidence, contextual alert is more valuable than one that generates hundreds requiring expert analysis. Also prioritize ease of deployment (agent overhead, configuration complexity) and integration with your existing workflows.

What’s the difference between runtime detection and runtime protection?

Runtime detection identifies suspicious behavior as it happens—anomalous processes, unusual network connections, unexpected file access. Runtime protection goes further by taking automated action: blocking suspicious processes, enforcing network policies, preventing file modifications. Detection tells you something is wrong; protection stops it. Effective tools provide both: detection for investigation and forensics, protection for automated response to known-bad behaviors. Ask vendors whether their “runtime security” is detection-only or includes enforceable protections.

Can I use open source tools for cloud security incident response?

Yes, but with tradeoffs. Open source tools like Falco (runtime detection), Kubescape (posture management), and Trivy (vulnerability scanning) provide specific capabilities with community validation and transparency. The challenge is correlation: you’ll need to build the integration layer that connects these tools into coherent attack stories. Many organizations use open source tools as sensors feeding into commercial platforms that provide correlation and investigation workflows. Kubescape, for example, serves as both a standalone open source tool and the foundation for ARMO’s commercial CADR platform.

How much does cloud security tool overhead affect application performance?

This varies significantly by tool architecture. Agentless scanning has zero runtime overhead but misses runtime behavior entirely. Agent-based tools using eBPF (like ARMO) typically consume 1-2.5% CPU and ~1% memory—low enough to be unnoticeable in most workloads. Older agent architectures or tools requiring sidecars can consume 5-10% or more. During POC, measure actual overhead in your environment with your workloads. Any vendor unwilling to share specific overhead metrics is hiding something.

What should I do if my current tools can’t detect multi-layer attacks?

You have three options: (1) Build correlation yourself by exporting data from each tool to a SIEM and creating custom correlation rules—this works but requires significant ongoing effort and expertise. (2) Layer a CADR platform on top of existing tools, using them as data sources while the CADR provides correlation and investigation. (3) Replace fragmented tools with an integrated solution. Option 2 often provides the fastest path to improved detection while you evaluate longer-term architecture. Start by mapping your current tooling against the Full Attack Story Framework to identify specific gaps.

How do compliance requirements affect cloud security tool selection?

Compliance (SOC 2, PCI-DSS, HIPAA, etc.) requires evidence of security controls, not specific tools. However, tools that provide continuous monitoring with audit trails make compliance easier than those requiring manual evidence collection. When evaluating tools, check: Does it provide audit-ready reports for your specific frameworks? Can it demonstrate continuous compliance or only point-in-time assessments? Does it integrate with your GRC platform? For Kubernetes compliance specifically, ensure the tool maps controls to Kubernetes-native constructs, not generic cloud infrastructure.

What’s the typical deployment time for cloud security tools?

This varies from hours to months depending on tool architecture and your environment. Agentless CSPM tools can be deployed in hours—they just need cloud API credentials. Agent-based runtime tools require deploying to each cluster, typically via Helm chart, and can take days to weeks depending on cluster count and change management processes. Full deployment (all clusters, tuned baselines, integrated workflows) typically takes 2-4 weeks for organizations with 5-20 clusters. Watch for vendors who promise “deploy in minutes” but don’t mention the tuning time required before the tool is actually useful.