|
ID
|
Domain
|
Focus
|
|
1
|
Model Identity & Lineage
|
Ensure full traceability of AI model origin, training data, architecture, and deployment history.
|
|
2
|
Autonomy Thresholds
|
Define acceptable levels of autonomy in Agentic AI. Trigger human-in-the-loop above defined levels.
|
|
3
|
Purpose Alignment
|
Validate AI behavior remains aligned with stated business, mission, or ethical objectives.
|
|
4
|
Trust Zones & Boundaries
|
Enforce isolation between AI execution domains (e.g., edge/cloud, classified/unclassified).
|
|
5
|
Data Provenance & Integrity
|
Guarantee source and chain-of-custody of training and inference data.
|
|
6
|
Model Risk Tiering
|
Categorize AI models based on impact (financial, safety, national security) and sensitivity.
|
|
7
|
Explainability Contracts
|
Mandate transparency levels for each model class. Attach explainability templates to outputs.
|
|
8
|
Autonomous Decision Logging
|
Capture immutable logs of autonomous decisions with reasons, inputs, and traceable justifications.
|
|
9
|
Override & Rollback Controls
|
Enable rollback or override for Agentic AI actions across systems. Emergency stop by design.
|
|
10
|
Synthetic Output Classification
|
Tag all outputs as synthetic or AI-generated at inference layer (metadata, watermarking, etc.).
|
|
11
|
System-of-Systems AI Mapping
|
Identify all interconnected AI agents, dependency graphs, and possible cascade failure paths.
|
|
12
|
Policy-Based Access to Agency
|
Define which agents get access to autonomous capability based on mission role, trust, or context.
|
|
13
|
Model Drift & Mutation Alerts
|
Monitor and alert for unexpected behavior, model drift, unauthorized updates, or adversarial shift.
|
|
14
|
Cryptographic Safeguards
|
Use post-quantum cryptography (PQC), AI key rotation, and secure enclaves for inference and control.
|
|
15
|
Compliance Anchors
|
Link AI decisions and actions back to compliance frameworks (NIST AI RMF, ISO/IEC 23894, etc.)
|
|
16
|
Embedded Ethics Protocols
|
Codify ethics guardrails into agent reasoning engines and training paradigms.
|
|
17
|
Real-Time Assurance Feedback Loops
|
Run live policy validation, explainability scoring, and safety check heuristics post-deployment.
|
|
18
|
Federated Lifecycle Controls
|
Control the AI lifecycle across multi-tenant, multi-org, or federated cloud environments.
|
|
19
|
Trust Decay Modeling
|
Detect degradation of system trust due to drift, edge instability, or data corruption.
|
|
20
|
Human Agency Preservation
|
Ensure the human operator remains the ultimate authority, with audit trails, veto paths, and recourse.
|