21 — Authorization-Aware RAG
Apply the requester’s current data entitlements throughout retrieval, reranking, generation, caching, and citation delivery.
Enterprise-Grade Security & Governance
Build permission boundaries, protect enterprise knowledge, and make every action accountable.
What is this agent allowed to do?
- Enforce authorization outside the model.
- Treat retrieved text and tool output as untrusted inputs.
- Keep evidence for every consequential action.
22 — Role-Based Access Control (RBAC)
Assign stable permissions to organizational roles and enforce those permissions at every agent tool and data boundary.
23 — Attribute-Based Access Control (ABAC)
Evaluate access from trusted subject, resource, action, and environment attributes when static roles cannot express the required business boundary.
24 — Prompt Injection Mitigation
Constrain untrusted instructions at data boundaries and prevent retrieved content from acquiring tool authority, secrets, or control over the agent workflow.
25 — Data PII Masking
Detect and transform personal data before model exposure while preserving task utility, protecting reidentification mappings, and measuring missed entities.
26 — VPC Peering / Private Links
Connect agent workloads to private services through deliberately scoped network paths while retaining application authentication, encryption, and egress control.
27 — Responsible AI Governance (ISO/IEC 42001)
Translate responsible-AI commitments into an operating management system with named owners, risk decisions, evidence, and continuous improvement.
28 — Deterministic Data Grounding
Bind generated claims to verified records and use deterministic computation for values that must be exact, current, and reproducible.
29 — Output Jailbreaking Guardrails
Apply an explicit output policy before publication and prevent unsafe model content from becoming executable instructions or unauthorized business actions.
30 — Non-Repudiation Logs
Create attributable, tamper-evident records of agent decisions and actions while recognizing that evidence strength depends on identity, keys, and independent custody.
31 — LLM Evaluation Frameworks (TruLens/Ragas)
Evaluate agent and RAG behavior with versioned datasets, component metrics, calibrated judges, and release criteria tied to actual task outcomes.
32 — Model Bias Auditing
Measure whether model behavior and downstream decisions differ across relevant groups, investigate causes, and validate mitigations within the actual application context.
33 — Token Rate Limiting
Enforce token-based admission and consumption budgets so individual requests and tenants cannot exhaust shared inference capacity or spend.
34 — Tenant Isolation
Prevent one tenant’s identity, data, state, tools, or resource consumption from crossing into another tenant’s execution boundary.
35 — Adversarial Prompt Testing
Continuously test adversarial inputs against the application’s actual trust boundaries, measuring unauthorized effects and preserving reproducible regression cases.
36 — Watermarking AI Outputs
Attach or detect provenance signals in generated content while treating watermark evidence as limited, attack-sensitive, and distinct from proof of truth.
37 — Zero-Data Retention (ZDR) Contracts
Translate retention commitments into enforceable endpoint selection, feature restrictions, logging controls, and evidence that the deployed data path matches the agreement.
38 — Data Lineage Tracking
Track how source data becomes chunks, embeddings, prompts, model outputs, and decisions so changes and incidents can be traced to their affected artifacts.
39 — Content Moderation API
Integrate content classification as a versioned policy service with explicit thresholds, contextual handling, latency budgets, and review paths.
40 — Explainable AI (XAI)
Provide audience-appropriate explanations grounded in actual evidence, rules, and execution records without presenting generated rationales as faithful internal reasoning.