AI agent security requires
enforcement, not observation.

What makes AI agent security different

When a human logs into a system and performs a task, there's a natural pause between intent and action. Security teams have built their tooling around that model — log in, do something, log out. Anomalies surface in SIEM. Access is provisioned through IAM. Compliance is documented after the fact.

AI agents don't work that way. A single agent invocation can trigger hundreds of tool calls in seconds — reading files, querying databases, sending messages, calling APIs — with no human review between steps. An agent with write access to a database and read access to email can exfiltrate an entire customer list in a workflow that completes before a human analyst even opens an alert.

The threat model is fundamentally different. The security model has to be too.

The five primary AI agent threat categories

Empirical red-team research on production-deployed AI agents has identified five attack categories that traditional security tools cannot address:

Why observation-only tools aren't enough

The AI security market is full of tools that observe what agents do and report on it. These tools have real value — audit trails, anomaly surfacing, compliance reporting. But they share a fundamental architectural limitation:

The table below maps the five threat categories to what different tool types can actually address:

What inline AI agent security looks like

Behavry sits as a transparent proxy between AI agents and the systems they access via Model Context Protocol (MCP). Every tool call passes through the governance stack before reaching its target. No agent code changes required — agents point their MCP configuration at the Behavry proxy.

Per-agent identity

Every agent receives a unique JWT RS256 credential with short-lived token lifetime. No shared API keys. Every tool call is cryptographically attributed to a specific agent instance, session, and requester channel before any policy evaluation begins.

Pre-execution policy enforcement

OPA Rego policies evaluate every tool call against per-agent rules before the request is forwarded. Policies encode intent, scope, resource boundaries, blast radius thresholds, and requester identity requirements — not just connection rules.

DLP scanning — outbound and inbound

26 sensitive data patterns are scanned before policy evaluation (outbound). After tool calls return, response bodies are scanned for injected instructions before results reach agent context (inbound). AWS credentials, GitHub tokens, SSNs, credit cards, PEM keys, and 20 more patterns — with cross-session fragment reassembly detection.

Behavioral baselining

Rolling per-agent baselines detect frequency spikes, novel resource access, error rate changes, data volume anomalies, and gradual risk score drift. Cross-session memory detects trust reset attacks that single-session monitoring misses entirely.

Decision Trace

Every action in a multi-agent pipeline is linked by parent event ID, causal depth, workflow session, and delegation chain. The Decision Trace is a causal chain of custody artifact — not a log. It is a proof. It can only be produced from an inline execution-path position.

AI agent security compliance requirements

Regulatory frameworks are increasingly specific about what AI agent security controls must look like:

• EU AI Act (Art. 9, 13, 14) — risk management systems, transparency, logging, and human oversight for high-risk AI systems. Enforcement is live.
• NIST AI RMF — agent identity and authorization (MG-2.1), human oversight of AI decisions (GO-1.7), behavioral monitoring against baselines (MG-2.2)
• SOC 2 (CC6.1, CC6.7, CC7.2–7.4) — access control enforcement, sensitive data controls, anomaly detection, and incident response
• GDPR Art. 32 / HIPAA §164.312 — technical safeguards for systems processing regulated data. AI agents with access to PII or PHI are in scope.

Behavry maps directly to all four frameworks with live controls — not documentation and not dashboard screenshots of what happened after the fact.