Ai Agent Compliance Controls

AI Agent Compliance Controls

Operating contract

Inputs

Outputs

• Produce: control matrix, test procedures, exceptions, evidence index, and remediation owners.

Capability and permission boundaries

Default to read-only analysis. Read only scoped records; redact secrets and regulated data. Writes, execution, network calls, production configuration, customer communication, billing changes, and delegation require explicit authority and an identified owner. Never widen tenant, time-window, or system scope implicitly.

Degraded mode

When required telemetry, evidence, execution, network access, or write authority is unavailable, return a partial result with each unassessed item labelled, preserve the safest existing state, and state the evidence or approval needed to continue. Never convert missing evidence into a pass.

Decision rules

Anti-Patterns

• Treating absent evidence as success. Fix: mark the check unassessed and name the missing source.
• Expanding one tenant or workflow to all tenants. Fix: enforce supplied scope at every query and action.
• Performing a production write during analysis. Fix: emit a reviewed change plan until authority is explicit.
• Reporting a metric without population, window, or source. Fix: attach all three.
• Hiding a failed threshold inside an average. Fix: report failure slices and the remediation owner.

Acknowledgement: Shared by Peter Bamuhigire, techguypeter.com, +256 784 464178.

Use When

• Design compliance controls for agent actions, logs, approvals, evidence, and access boundaries.
• Map agent operations to SOC 2, ISO 27001, HIPAA, or control testing requirements.
• Prepare control evidence and audit-ready narratives for agentic AI systems.
• Prove kill-switch, red-team, eval-drift, and restoration drills ran on cadence with signed pass/fail evidence (references/ai-agent-drill-evidence-and-cadence/).
• Prove every irreversible agent action had a prior, authorised, linked approval and emit the SOC 2 PI1.1 gap register (references/ai-agent-approval-audit-completeness/).
• Prove a data-subject erasure removed all agent memory tiers, vectors, replicas, and subprocessor copies, with a signed proof-of-erasure pack (references/ai-agent-memory-erasure-proof/).

Do Not Use When

• The work is not AI-specific or agentic-AI-specific.
• A narrower retained AI parent skill fits the request better.

Required Inputs

• Product, tenant, user, data, risk, and operational context relevant to the AI workflow.
• Target artifact: design, implementation plan, audit, test strategy, UX flow, commercial policy, or runbook.
• Constraints from security, privacy, reliability, billing, support, and compliance stakeholders when relevant.

Workflow

1. Read this SKILL.md first.
2. Load references/routing.md to select the absorbed child reference that matches the task.
3. Load only the selected child reference files needed for the current request.
4. Produce execution-oriented output with assumptions, risks, evidence, and next actions where relevant.

Quality Standards

• Keep routing explicit: name which reference files were used when the work depends on absorbed material.
• Preserve tenant isolation, auditability, cost controls, safety gates, and operational evidence when they matter.
• Prefer concrete contracts, checklists, tables, schemas, runbooks, and decision records over broad summaries.

Anti-Patterns

• Loading every absorbed reference by default.
• Treating AI-specific billing, compliance, safety, or UX concerns as generic SaaS work without checking AI failure modes.
• Hiding retired skill names; old slugs must remain discoverable through references/routing.md.

Outputs

• A concrete deliverable matched to the request: architecture, implementation plan, audit, policy, runbook, UX flow, test strategy, or operating model.
• The selected consolidated reference files and any assumptions, risks, evidence requirements, or follow-up actions that affect execution.

References

• references/routing.md maps retired child skill slugs to their consolidated reference folders.
• Load references/eu-ai-act-and-iso-42001-agent-overlay.md when a feature may be EU AI Act high-risk or a customer asks for ISO/IEC 42001 alignment: classification, Arts. 9-15/19/72/73 mapped to existing controls, application dates.
• Load references/ai-agent-drill-evidence-and-cadence/entrypoint.md when turning drills into audit evidence: drill registry, minimum cadence, overdue paging, signed drill packs. Drill scenario design stays in ai-agent-safety-and-red-team and ai-incident-response.
• Load references/ai-agent-approval-audit-completeness/entrypoint.md when an auditor asks for proof that irreversible actions were approved before execution: gap-detection job, backfill detection, PI1.1 pack.
• Load references/ai-agent-memory-erasure-proof/entrypoint.md when an erasure request touches agent-derived memory: cascade verification job, residue probes, signed proof pack.
• Load references/continuous-control-monitoring.md when proving controls stayed effective across an audit window: declared-vs-observed drift checks, compliance deploy gate, framework edition register, LLM provider re-verification.

Consolidated Child References

• Load references/routing.md to map retired AI child skill slugs to their reference modules.

Evidence Produced