What to Look for in an AI Workload Security Tool: The Complete Buyer’s Guide

You’re evaluating AI workload security tools and every demo looks the same. Vendor A shows you an AI-SPM dashboard. Vendor B shows you a nearly identical AI-SPM dashboard with slightly different branding. Vendor C shows you posture findings with an “AI workload” tag that wasn’t there last quarter. You’re 45 minutes into each call and you still can’t tell which tool would actually detect a prompt injection attack on your production AI agent versus one that would just alert you to “unexpected process started.”

But strip away the AI branding and what you often find is the same CSPM, the same agentless scanning, the same configuration checks — repackaged with an AI label on the marketing page. The shift from CNAPP to CADR reflects exactly this gap: posture-only tools can’t protect workloads that behave autonomously.

Before going further, one clarification that will save you hours of evaluation time: there is a critical difference between AI-for-security tools (which use AI to improve threat detection across all workloads) and security-for-AI tools (which specifically protect AI workloads from AI-specific threats). Most of the vendor noise in this space comes from conflating these two categories. A tool that uses machine learning to improve alert triage is valuable, but it’s not what you need to protect an AI agent from prompt injection. This guide focuses exclusively on the second category: tools that secure AI workloads themselves.

This evaluation is different from any other cloud security buying decision you’ve made: AI workloads don’t just run. They behave. That behavioral reality is what makes cloud-native security for AI workloads a fundamentally different challenge than container security — and what makes most existing cloud security tools blind to the real risk.

AI Workloads Are Not Just Containers with ML Frameworks

Here’s the assumption baked into every traditional container security tool: an application’s behavior is bounded by the deterministic algorithms it executes. You deploy a container, it runs the code you wrote, it does what you expect. If something unexpected happens — an unfamiliar process starts, an unusual network connection fires — that’s an anomaly worth investigating.

AI agents break this assumption completely.

An AI agent running in your Kubernetes cluster can be instructed — through a prompt, through data it ingests, through a tool it calls — to execute code that nobody wrote ahead of time. It can traverse permission boundaries that looked safe in your IAM review because the review assumed deterministic behavior. It can interact with external APIs in ways that weren’t in the design spec because the design spec assumed a human would be making those decisions.

Think about what that means for your security tooling. When an AI agent starts a new process, your container security tool flags it as an anomaly. But was that process triggered by a legitimate workflow change? A developer updating the agent’s tool configuration? A prompt injection attack that just caused the agent to execute arbitrary code? Your existing tools can’t tell the difference — because they have no concept of what a prompt is, what a tool invocation looks like, or what “normal behavior” means for an AI agent that’s inherently non-deterministic. This runtime context is what separates tools that understand AI workloads from tools that just scan them.

The attack vectors specific to AI agents — what OWASP catalogs in their Top 10 for Agentic Applications — are categorically different from the container-level threats that existing runtime tools were built to detect. Agent escape, prompt injection and manipulation, tool misuse and API abuse, data exfiltration through AI-mediated flows: these require AI-specific detection categories, not generic container anomaly alerts repurposed for workloads that happen to run AI frameworks.

Why Vulnerability Scanning and Posture Management Alone Fall Short

If your current cloud security stack relies on agentless scanning — and if you’re using one of the major CNAPPs, it probably does — there’s a structural limitation you need to understand: agentless scanning is constrained to what cloud APIs expose. 

That’s fine for catching misconfigured S3 buckets and overly permissive IAM roles. It’s not fine for understanding what an AI agent actually does in production. Agentless scanning can tell you that an AI workload has admin permissions. It can’t tell you whether those permissions are necessary for the agent’s actual behavior or whether they’re exploitable attack surface — because it never watches the workload operate. This structural blind spot is why traditional cloud security consistently fails for AI workloads — it was built for a deterministic world.

This creates a noise problem that security teams know well. Your posture management tool surfaces 500 findings on your AI workloads. Most are theoretical: yes, that permission could be exploited, but is it? Is the agent actually using it? Is it reachable? Without runtime behavioral data, you’re triaging based on theoretical risk rather than actual risk. That’s how critical findings get buried under hundreds of false positives. If your tool is surfacing hundreds of findings without runtime context, you’re experiencing exactly why CSPM alone can’t secure AI workloads.

The 4-Pillar Evaluation Framework

Most security teams evaluating AI workload security tools use generic cloud security criteria: “Does it integrate with our CI/CD pipeline? Does it support our cloud provider? Does it check compliance boxes?” Those questions matter, but they don’t help you evaluate whether a tool can actually protect AI workloads. For that, you need a framework built around the specific capabilities that AI workload security demands.

The framework below organizes these into four pillars, arranged in the order they need to be implemented. This isn’t arbitrary — each pillar depends on the one before it. You must see before you can assess risk. You must assess risk before you can detect threats accurately. And you must detect threats before you can enforce controls safely.

Pillar	Core Evaluation Question	Why It Matters for AI Workloads
1. Observability	Can you see what AI agents exist and what they actually do at runtime?	AI agents interact with tools, APIs, and data in ways that static configuration can’t predict. Without runtime visibility, you’re securing a workload you don’t understand.
2. Posture	Can you assess AI-specific risk before an incident occurs?	AI workloads are deployed with excessive permissions, untracked dependencies, and no behavioral baseline. You need to know the gap between what an agent can do and what it actually does.
3. Detection	Can you detect AI-specific attacks, not just generic container anomalies?	Prompt injection, agent escape, and tool misuse look nothing like traditional container threats. Generic runtime detection is blind to the AI-specific context that makes these attacks identifiable.
4. Enforcement	Can you enforce least privilege on AI agents without breaking production?	AI agents are non-deterministic. You can’t write enforcement policies before understanding their behavior. You need to observe first, then enforce based on evidence.

These four pillars also function as a maturity model — a staged progression from first visibility to full enforcement:

Stage	Goal	What Your Tool Must Do	Output
1. Discovery	Know what AI workloads exist	Auto-detect AI agents, frameworks, and inference servers across all clusters	Complete runtime AI inventory (AI-BOM)
2. Baselining	Understand normal behavior	Build behavioral profiles from observed runtime activity, not declared config	Behavioral DNA for each AI workload
3. Detection	Detect AI-specific threats	Enrich detections with AI context; identify agent escape, prompt injection, tool misuse	Context-rich AI incidents, not generic alerts
4. Enforcement	Control blast radius	Promote behavioral baselines into enforcement policies; per-agent sandboxing	Production-safe least privilege for AI agents

The order matters. Any vendor that pushes enforcement before observability is selling you the policy paralysis problem (more on that in Pillar 4). Any vendor that offers detection without behavioral baselines will flood you with false positives. And any vendor that offers posture assessment without runtime data is giving you theoretical risk, not actual risk. Use both tables as your primary evaluation structure.

Evaluating Each Pillar: What to Look For and What to Watch Out For

Pillar 1: Observability — Can You See What AI Agents Actually Do?

The first question to ask any vendor is deceptively simple: How does your tool discover AI workloads?

If the answer involves manual tagging, classification by your team, or reliance on cloud API metadata, you have a problem. AI workloads are proliferating faster than any manual inventory process can keep up. Developers are deploying LangChain agents, spinning up inference servers, connecting MCP tool runtimes — often without notifying security. Any observability solution that relies on you knowing about a workload before it can see it will miss the shadow AI deployments that represent your biggest blind spot.

What to look for:

• Automatic runtime AI workload discovery. The tool should detect AI agents, inference servers, frameworks (LangChain, AutoGPT, CrewAI, etc.), and tool runtimes across all connected clusters without manual configuration. ARMO’s platform, for example, performs Kubernetes-first discovery that automatically detects agents, inference servers, and MCP tool runtimes as they appear — no tagging required.
• A runtime-derived AI-BOM (AI Bill of Materials). Most buyers haven’t encountered this concept yet, but it’s critical. An AI-BOM goes beyond your static SBOM to capture what an AI workload actually uses at runtime: the models it loads, the RAG sources it connects to, the external tools and APIs it calls. If your tool’s inventory only reflects what’s declared in deployment manifests, it’s missing everything that makes AI workloads uniquely risky.
• Prompt and tool call visibility. Can the tool show you what prompts are executing, which tools the agent is invoking, and where data is flowing? This behavioral layer is what static tools completely miss.
• An agent execution graph that maps the complete chain: Agent → Tool → API → Data → Identity. This graph becomes the foundation for everything else — posture assessment, detection, and enforcement all depend on understanding these relationships. It should also map every AI workload to its Kubernetes identities, service accounts, IAM roles, and network paths, so you can assess the blast radius if an agent is compromised.

Red flag: If the vendor’s “discovery” page shows a list of cloud resources with an AI tag, that’s cloud asset inventory, not AI workload observability. The difference is behavioral visibility — seeing what agents actually do, not just that they exist.

The reason observability must come first is practical: every other capability in the evaluation depends on it. Runtime-informed posture assessment requires runtime observability data. 

AI-specific detection requires knowing which workloads are AI agents. Progressive enforcement requires behavioral baselines that only observability can build. A tool that skips or shortcuts this stage will underperform on every stage that follows.

Pillar 2: Posture — Can You Assess AI Risk Before an Incident?

Once you have observability, the next question is: Does the tool distinguish between what an AI agent can do and what it actually does?

This is where most AI workload security evaluations go wrong. Vendors will show you AI Security Posture Management (AI-SPM) dashboards that surface misconfigured permissions, exposed network paths, and vulnerable AI frameworks. That’s useful. But if the posture assessment is purely configuration-based — checking IAM policies, network rules, and framework versions without any runtime behavioral data — it’s doing generic CSPM with an AI label.

The difference matters because AI workloads are routinely deployed with permissions that look excessive on paper but may be legitimate in practice (or vice versa). An AI agent that has write access to a database might need it for its actual workflow, or that permission might be leftover from a development sprint and represent real exploitable surface. You can’t tell the difference without watching the workload operate.

What to look for:

• Runtime-informed posture. The tool should compare declared permissions against observed behavior to create a meaningful risk gap analysis. “This agent has access to 47 APIs but only uses 3” is significantly more useful than “This agent has access to 47 APIs.”
• AI supply chain risk assessment. Your tool should scan AI-specific components — LangChain, inference server runtimes, MCP connectors — for known CVEs, malicious skills, and vulnerable rules that standard vulnerability scanners may not cover.
• Behavioral baseline and drift detection that defines “normal” AI agent behavior from observed patterns and surfaces deviations as risk signals before they become incidents.

Red flag: If the vendor’s posture assessment looks identical to what they offer for any other cloud workload — misconfiguration checks, IAM analysis, vulnerability scanning — with no AI-specific behavioral layer, it’s not AI workload posture management. It’s CSPM that happens to scan the namespaces where your AI workloads run. 

Contrast this with ARMO, whose AI-SPM builds on its Kubernetes security posture management foundation by comparing declared permissions against observed behavior patterns — creating a risk gap analysis that distinguishes theoretical exposure from actual risk. That runtime-informed approach is how their customers routinely eliminate over 90% of CVE noise through reachability analysis.

Pillar 3: Detection — Can You Detect AI-Specific Attacks?

This is where the evaluation gets the most nuanced, and where most vendor claims fall apart under scrutiny. Ask any AI workload security vendor this question: If an AI agent gets compromised via prompt injection, will your tool detect “prompt injection attack on AI agent X” or just “unexpected process started”?

The answer tells you everything about whether the tool has real AI-specific detection or is repurposing generic runtime alerting.

The AI-Native vs. AI-Aware Distinction

This is a distinction nobody in the market is clearly drawing, and it’s the single most important differentiator to understand when evaluating detection capabilities.

AI-aware tools have taken existing container and cloud detection rules and applied them to workloads that happen to run AI frameworks. They’ll alert on unusual process execution, unexpected network connections, and privilege escalation — valid signals, but not AI-specific. An AI-aware tool treats a prompt injection the same way it treats any unexpected process because it has no concept of what a prompt is. It doesn’t know that the “unexpected API call” was initiated by a malicious instruction embedded in a document the agent was processing. It just sees a network anomaly.

AI-native tools have built detection categories specifically for the attack vectors that AI agents introduce. Prompt injection — ranked the #1 risk in the OWASP Top 10 for LLM Applications since the list’s inception — requires detection that understands prompts. Agent escape attempts require detection that understands agent boundaries. Tool and API misuse requires detection that understands which tools an agent is authorized to use. When an AI-native tool detects a threat, it tells you what kind of AI attack it is, which agent was targeted, what prompt triggered it, which tool was misused, and what data was involved.

What this looks like in practice:

A data analysis agent processing a batch of customer support tickets ingests a ticket containing a carefully crafted prompt injection. The injected instruction causes the agent to invoke its database tool with a query the agent was never designed to execute — pulling customer PII from a table outside its normal scope. The agent then attempts to exfiltrate this data through an API call to an external endpoint it has never contacted before.

An AI-aware tool sees: “Unexpected database query detected” + “Anomalous outbound network connection.” Two separate generic alerts in two different dashboards. A security analyst spends 45 minutes correlating them before realizing they’re the same incident.

An AI-native tool sees: “Prompt injection attack on Data Analysis Agent → Unauthorized database tool invocation (customer_pii table) → Data exfiltration attempt to external-endpoint.com.” One context-rich incident with the agent identity, the injected prompt, the misused tool, the accessed data, and the full identity chain. Investigation time: minutes, not hours.

That’s the difference between a generic container alert and an actionable AI security incident.

Minimum requirements for AI-native detection:

• AI-specific detection categories covering prompt injection, agent escape, tool misuse, behavioral anomaly, and data exfiltration through AI-mediated flows. If the detection categories are the same ones the tool uses for non-AI workloads, you’re looking at AI-aware, not AI-native.
• Context-rich incidents that include the agent identity, the prompt involved, the tool invoked, the data accessed, and the full identity chain. A detection that says “unexpected process started” without AI context is not actionable for AI-specific threats.
• Behavioral baseline detection — not just signature matching. AI agents drift. Their behavior evolves as prompts change, tools are updated, and data sources shift. Detection must identify deviations from established behavioral profiles, not just match known attack patterns.

The question to ask any vendor: Show me your AI-specific detection categories. Not your container detection applied to AI workloads — your detections built for AI-specific attacks.

Go/No-Go Question: If an agent receives a prompt injection that causes it to call an unauthorized API, will the tool surface that as an AI-specific incident — or as a generic “unauthorized network connection”? If the vendor can’t demonstrate the former, they’re offering AI-aware detection, not AI-native detection. 

For many organizations, that might be sufficient as a starting point. But if you’re deploying AI agents with real autonomy in production, the gap matters. For CISOs building an AI agent deployment strategy, this distinction has direct implications for safely deploying AI agents in production.

ARMO’s Cloud Application Detection and Response (CADR) platform is built AI-native. Every runtime detection finding is enriched with AI context: whether the incident occurred inside an AI agent, which agent was involved, what prompt was executing, and what tool or API was invoked. 

Detection categories include specific rules for agent escape attempts, prompt injection and manipulation, tool misuse and API abuse, and data exfiltration through AI-mediated flows. Instead of presenting isolated alerts that a security analyst must manually correlate, the platform stitches together cloud events, container events, Kubernetes events, and application events into a coherent attack story — which is how ARMO delivers the 90%+ reduction in investigation and triage time that their customers report.

Pillar 4: Enforcement — Can You Control AI Agents Without Breaking Production?

Enforcement is where evaluation conversations typically go wrong, because vendors love to demo enforcement features without addressing the hardest problem: how do you know what policies to write?

There’s a term for this that every security team deploying AI agents in production will recognize: policy paralysis. You want to enforce least privilege on your AI agents. You know you should constrain what tools they can access, what APIs they can call, what network destinations they can reach. But you can’t write those policies because you don’t yet understand what the agents actually do.

The result is predictable: either you write overly restrictive policies that break production (engineering escalates, security backs off, the policies get removed), or you write permissive policies that leave security gaps (but at least nothing breaks). Neither outcome is acceptable.

What to look for: a tool that solves the policy paralysis problem by letting you observe before you enforce.

• Progressive enforcement workflow. Start in visibility-only mode, accumulate behavioral data on your AI agents over time, then promote observed behaviors into enforcement policies with confidence. This Observe-to-Enforce workflow is the only approach that eliminates the policy paralysis problem.
• Kubernetes-native sandboxing. Enforcement should work at the kernel level, typically using eBPF-based runtime security, with zero code changes required. If enforcement requires modifying your application code, injecting sidecars, or redeploying workloads, the operational overhead will prevent adoption.
• Per-agent granularity. Different AI agents need different policies. Your customer support chatbot has a very different legitimate behavior profile than your data analysis agent. The tool should support per-agent guardrails based on each agent’s observed behavior, not a one-size-fits-all policy.
  

Red flag: If the tool offers enforcement but requires you to define all policies manually before you understand agent behavior, you’re back to policy paralysis. Ask the vendor: “What happens if I don’t know what policies to write yet?” If the answer is anything other than “You observe first and we help you generate policies from observed behavior,” keep looking. If you want a printable version of these evaluation criteria to bring into vendor calls, download the AI workload security evaluation checklist.

ARMO’s sandboxing architecture addresses this directly. You deploy in visibility-only mode, the platform builds behavioral profiles (what ARMO calls “Application Profile DNA”) for each AI agent based on actual observed behavior, and then you promote those profiles into eBPF-based enforcement policies with zero code changes. 

As our CTO Ben puts it: “A security practitioner can see that an agent has been running for a week, see exactly what tools and APIs it uses, and then lock it down to only those behaviors.” 

That’s enforcement based on evidence, not guesswork — running at 1–2.5% CPU and 1% memory overhead, which is within the performance budget most platform teams accept.

Runtime-First vs. Declarative-Only: The Architectural Divide

Behind the marketing claims and feature lists, there’s a fundamental architectural divide that determines whether any of the four pillars can actually deliver. The 2025 Latio Cloud Security Market Report formally defines CADR as the evolution of cloud workload security — a shift from static visibility to runtime-driven risk reduction. The architectural divide between runtime-first and declarative-only tools is what makes that shift possible (or not).

Capability	Runtime-First Tools	Declarative-Only Tools
AI workload discovery	Automatic detection at runtime via eBPF sensor	Relies on cloud API metadata or manual tagging
Agent behavior visibility	Observes actual prompts, tool calls, API interactions, and data flows	Can only see declared configuration and permissions
Risk prioritization	Actual risk based on runtime reachability and observed behavior	Theoretical risk based on static CVE scoring and permission analysis
AI-specific detection	Detects prompt injection, agent escape, tool misuse with AI context	Detects generic misconfigurations; cannot distinguish AI-specific attacks
Enforcement approach	Progressive: observe behavior, then enforce based on evidence	Static: write policies manually before understanding agent behavior
Performance overhead	1–2.5% CPU, 1% memory (eBPF-based)	Zero runtime overhead (but zero runtime visibility)

Neither approach is inherently wrong — declarative-only tools are perfectly fine for posture management on traditional cloud workloads. But for AI workloads specifically, the behavioral gap is a structural limitation. If the tool never watches the workload operate, it cannot assess the risk dimensions that make AI workloads uniquely dangerous: non-deterministic behavior, dynamic tool usage, and autonomous decision-making.

Some vendors are trying to bridge this gap by acquiring or building runtime capabilities on top of their declarative foundations. That’s a reasonable strategy, but maturity matters. A runtime capability that’s been shipping for six months is not equivalent to one refined over years of production deployments. 

The Latio report’s global survey found that most teams are satisfied with visibility but disappointed in workload and application protection — which suggests that the declarative-to-runtime transition is still early for many vendors. When evaluating vendors in this transitional period, ask: 

How long has your runtime capability been in production? How many customers are using it for AI workload detection specifically?

Platform and Industry Considerations

The four pillars apply regardless of where your AI workloads run, but specific cloud platforms and regulated industries add complexity that affects tool selection. Each major cloud provider — AWS with GuardDuty for SageMaker, Azure with Defender for AI, Google Cloud with Security Command Center for Vertex AI — now offers native AI security capabilities. 

The key evaluation question is the same: do they provide runtime behavioral visibility, or just posture scanning? In most cases today, native tools provide strong posture management but limited runtime detection for AI-specific attack vectors. For production AI agents that require behavioral baselining and progressive enforcement, you’ll likely need a specialized tool that works across clouds. For platform-specific evaluation guidance, see the detailed breakdowns for AWS, Azure, and GKE.

If you’re in a regulated industry, your evaluation includes additional criteria: continuous compliance monitoring against frameworks like HIPAA, PCI-DSS, or SOC2; audit-ready evidence generation; data residency controls; and the ability to demonstrate AI-specific security controls to auditors. 

The core 4-pillar framework still applies, but the output requirements are stricter. ARMO covers this with 260+ purpose-built Kubernetes compliance controls across CIS, NSA, NIST, SOC2, PCI-DSS, HIPAA, and GDPR, with continuous automated monitoring and audit-ready evidence exports. Teams in regulated industries should layer in the additional requirements covered in the guides for healthcare and financial services.

What This Adds Up To

Throughout this guide, ARMO has appeared at each pillar because our platform architecture maps directly to this evaluation methodology. That’s not coincidental — ARMO’s approach to AI workload security was built around the same progression (see first, then understand, then detect, then enforce) that this framework recommends. Rather than restating each capability, here’s what the combined platform delivers:

Quantified outcomes: 90%+ CVE noise reduction through runtime reachability analysis. 90%+ faster investigation and triage through LLM-powered attack story generation. 80%+ reduction in issue overload through runtime-based prioritization. All at 1–2.5% CPU and 1% memory overhead, which is within the performance budget most platform teams accept.

The platform is built on Kubescape, one of the most widely adopted cloud-native open-source security projects, used by more than 100,000 organizations with 11,000+ GitHub stars. 

ARMO’s AI workload security is shipping in three phases: Phase 1 (Kubernetes AI Security) is available now, Phase 2 (Cloud AI Security) extends coverage to broader cloud workloads, and Phase 3 (Managed AI and SaaS AI) extends it to managed LLMs and external AI services.

Your Next Steps

AI workloads introduce a fundamentally different threat model than traditional cloud workloads. Static scanning, posture-only assessment, and generic container alerting leave critical blind spots that AI-specific attack vectors will exploit. That’s the structural reality of workloads that make autonomous decisions, execute generated code, and interact with external systems in non-deterministic ways.

Walk through each vendor against the four pillars — observability, posture, detection, enforcement — and require AI-specific capability at every layer. If a vendor can’t demonstrate real capability across all four, with AI-specific context rather than repurposed cloud security features, they’re not ready to protect AI workloads in production.

See how ARMO addresses all four pillars. Book a demo at ARMO, see how ARMO compares against other AI workload protection platforms.

Frequently Asked Questions

What’s the difference between AI workload security and using AI in security tools?

AI-for-security tools use machine learning to improve threat detection across all workloads. Security-for-AI tools specifically protect AI workloads from AI-specific threats like prompt injection and agent escape. Most vendors conflate these categories to claim “AI security” without building AI-specific detection. If a vendor’s AI capability is limited to using LLMs to summarize alerts, that’s not protection for your AI agents in production.

Can my existing CNAPP or CSPM tool protect AI workloads?

Partially. Your CNAPP can scan configurations, flag misconfigured permissions, and identify vulnerable AI framework versions — the same posture management it does for any workload. What it can’t do is observe what AI agents actually do at runtime, detect AI-specific attacks like prompt injection, or enforce least privilege based on observed behavior. For AI workloads that behave non-deterministically, that runtime gap is where the most dangerous attack vectors live.

What is an AI-BOM and why do I need one?

An AI-BOM (AI Bill of Materials) is a runtime-derived inventory of what your AI workloads actually use in production: models, frameworks, RAG sources, external tools, and APIs. It goes beyond a static SBOM because AI workloads frequently use components at runtime that aren’t declared in deployment manifests. Without one, your security team is working from an incomplete picture of what’s actually running.

What is policy paralysis and how do I avoid it?

Policy paralysis happens when security teams want to enforce least privilege on AI agents but can’t write the policies because they don’t understand what the agents actually do. AI agents are non-deterministic, so you can’t define “normal” from documentation alone. The solution is an Observe-to-Enforce workflow: deploy in visibility-only mode, let the tool build behavioral baselines from observed activity, then promote those baselines into enforcement policies. Any tool that requires you to define all policies upfront will reproduce this problem.

How much performance overhead does runtime AI workload security add?

eBPF-based runtime sensors typically add 1–2.5% CPU and approximately 1% memory overhead — within the performance budget most platform teams accept. Declarative-only tools add zero runtime overhead but provide zero runtime visibility. For AI workloads where the most dangerous attack vectors exist at runtime, the performance cost of behavioral monitoring is far less than the risk of operating blind.

Do I need a separate tool for AI workload security?

If you’re running a small number of AI agents in non-critical environments, your existing CNAPP plus manual monitoring may suffice. But if you’re deploying AI agents with real autonomy in production — agents that make decisions, call external APIs, and access sensitive data — you need AI-specific capabilities across all four pillars. The most practical approach is a platform that covers both traditional Kubernetes security and AI workload security from the same runtime foundation, so you’re not managing separate tools and data streams.

What compliance frameworks apply to AI workloads?

The same frameworks that apply to your other cloud workloads — CIS, NIST, SOC2, PCI-DSS, HIPAA, GDPR — plus emerging AI-specific governance requirements. The challenge is that auditors are beginning to ask about AI-specific controls: how you monitor agent access, enforce boundaries on autonomous behavior, and demonstrate least privilege. Your tool should map AI-specific security controls to regulatory requirements with continuous automated monitoring, not periodic scans.