Agent Registry / Inventory

Maintain a live catalogue of all agents operating in the system.
Each record should include:

• Agent ID (unique identifier)

• Owner / responsible team

• Purpose / mission scope

• Data and tool access domains

• Version / build hash

• Risk rating and current trust status

Why: A registry ensures accountability, discoverability, and auditability — key for both internal security and regulatory reporting.

Identity & Tokenization

Assign each agent a cryptographically verifiable identity — distinct from human or service user accounts.

• Use workload identities (mTLS, SPIFFE, DID, etc.)

• Issue short-lived, scoped tokens tied to specific APIs or datasets

• Store private keys or signing material in HSM/KMS, never in plain config

• Rotate identities automatically and revoke on anomaly or decommission

Why: Agent identity must be independently provable to support zero-trust operations and non-repudiation of actions.

Authorization & Policy Enforcement

Move beyond static roles. Implement dynamic authorization using:

• Attribute-Based Access Control (ABAC) or Policy-Based Access Control (PBAC)

• Policies evaluating context (dataset classification, time, risk state, task type)

• Centralized policy engines (e.g., OPA, Cedar)

Example: Agent may read “TrialData” only if approved for project X, within research phase, and from trusted environment.

Why: Agents act dynamically; authorization must evolve with context.

Data and Model Safeguards

Define strict rules for what data and models each agent can access or modify:

• Approved model and dataset lists per risk tier

• Controlled retrieval layers (e.g., gated vector store access)

• Prompt injection & output filtering for LLM components

• Monitoring for state drift or unusual tool invocation patterns

Why: Agents manipulate sensitive data and language models — prevent unintentional data leakage or corrupted model behaviour.

Observability, Audit, and Traceability

Log every significant event:

• Which agent acted, what it did, which data/tool it touched, and the outcome

• Store logs with cryptographic integrity (e.g., WORM or blockchain-style attestations)

• Integrate with SIEM, data observability, and AIOps pipelines

• Enable behavioural baselining and anomaly detection

Why: Transparent traceability allows you to prove compliance, detect abuse, and evaluate model/agent reliability over time.

Human Oversight and Kill Switches

Define boundaries of autonomy and escalation rules:

• Human-in-loop (HITL) for high-risk or irreversible actions (publishing, data export, external communications)

• Kill switch / emergency stop for runaway or compromised agents

• Policy-based throttling or cooldowns for unsafe behaviour

Why: Even advanced agents require containment; humans retain ultimate authority.

Lifecycle Management and Provenance

Treat agents like software artifacts:

• Version every build (image digest, policy hash, dataset signature)

• Attest build provenance — who built, approved, and deployed it

• Revoke tokens and archive logs upon retirement

• Periodically re-validate aging agents against current compliance rules

Why: Without lifecycle discipline, dormant or outdated agents become unmonitored security liabilities.

Regulatory and Ethical Alignment

Map your security controls to relevant standards:

• General: ISO/IEC 27001, 42001, NIST AI RMF

• Sectoral: HIPAA, GDPR, GxP, GLP, FINMA, MiFID II, or local equivalents

• Research ethics: transparency, consent, reproducibility, data minimization

Maintain documentation for audits and AI governance boards.

Why: Agent systems must meet both technical security and ethical or legal accountability standards.

Optional Additions for Mature Platforms

Federated Trust Frameworks — enable secure agent collaboration across institutions or clouds (via verifiable credentials, DIDs).

Behavioral Sandboxing — run new or experimental agents in constrained execution environments before granting full permissions.

Continuous Risk Scoring — dynamically assess each agent’s operational trust based on recent behaviour, model drift, or policy violations.