10 Patterns to Secure AI Agents
Most incidents won't come from bad models. They'll come from agents with over-access, zero tracing, no guardrails, and no validation.
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1
Least-Privilege & Tool Mediation
ACCESS- • One agent, one job
- • Minimal tools & data
- • Gateway with allowlists & rate limits
2
Context Boundaries
DATA- • Strict retrieval scopes
- • No cross-project memory
- • Time-bounded sensitive data GDPR & EU AI Act purpose limits
3
Escalation & Human Oversight
CONTROL- • Agents stop, humans decide
- • Queues, SLAs, rich handoff context
- • High-impact & low-confidence triggers
4
Plan-Then-Execute & Output Filtering
CONTROL- • Model proposes plan
- • Deterministic layer validates
- • Only approved steps run
5
Isolation
ACCESS- • Sandboxed reasoning
- • Restricted networks
- • Controlled orchestration layer only
6
Input Defense
DATA- • Treat every input as hostile
- • Enforce schemas
- • Strip control sequences
- • Reject weird structures
7
Policy-as-Workflow
CONTROL- • Policies are code, not just slides
- • Who, what data, what to log → hard checks
8
Audit & Traceability
OPS- • Explain every agent action
- • No explanation = demo, not system
9
Identity & Secrets
ACCESS- • Agents = identities with roles
- • Short-lived tokens
- • Secrets in vaults, never in prompts
10
Continuous Testing & Monitoring
OPS- • Adversarial & leakage tests
- • Track error rates & escalations
- • Privacy-compliant telemetry
- • Failure analysis → updated rules
“Agents are not dangerous by nature. They are dangerous when you design them without patterns and limits.”
If you're building agents for production, start with these 10.