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Regex prompt sanitization fails because LLM payloads are not strings — they are encoded instructions, and a language model interprets meaning, not bytes.

Most CrowdStrike deployments have the same blind spot. Endpoints are covered, IAM behavior is logged, network traffic is monitored. But the AI agents running on that infrastructure, making thousands of decisions per day, generate zero signals in Falcon unless something hits the endpoint in a way that looks like traditional malware.

A Data Protection Impact Assessment (DPIA) for an AI workflow is the GDPR Article 35 record that documents the data flows specific to LLM applications — prompts, completions, embeddings, tool calls, RAG retrieval — together with the legal basis, retention schedule, identified risks, and the mitigations that bring those risks down to an acceptable level.

AI compliance in 2026 is what a CISO must prove to a board, an auditor, or a regulator about the AI systems running in production. This is the eight-item list of evidence patterns that survives the 2026 audit cycle. Each item below names what to prove, the canonical evidence pattern, and the framework references that ask for it.

Today’s security teams face the challenge of identifying not just known threats, but also emerging and unknown threats that can bypass conventional defenses. This is where artificial intelligence (AI) and machine learning (ML) are transforming the field.

Agents are powerful. They write to production databases. They call APIs. They move files. They trigger workflows. Unlike traditional applications, which are deterministic systems where every code path gets reviewed before deployment and behavior is predictable, agents behave in non-deterministic and evolving ways at runtime. They make decisions. They execute. We react.

Most security teams haven’t inventoried their AI agents, let alone assessed the risks those agents introduce in enterprise environments. That’s a problem because AI agents in production environments have something attackers want: credentials, access, and the ability to take action autonomously.

Shadow AI is unsanctioned LLM usage inside an enterprise — business units calling api.openai.com, generativelanguage.googleapis.com, api.anthropic.com, or hosted-LLM endpoints from spreadsheets, scripts, browser plug-ins, and self-built apps without the SOC's knowledge.

Most security teams haven’t inventoried their AI agents, let alone assessed the risks those agents introduce in enterprise environments. That’s a problem because AI agents in production environments have something attackers want: credentials, access, and the ability to take action autonomously.