AI-TRiSM (AI Trust, Risk and Security Management) is the operational core of trustworthy AI: manage risk, enforce security, monitor quality—and run it in a way that stays inspectable over time.
Many teams start with “model accuracy”. In sensitive and regulated environments, that’s not enough. Trust is a system outcome: controlled identities, controlled data, controlled model change—and evidence you can show on demand.
Trust isn’t a claim. It’s controlled inputs, hardened outputs, measurable operations—and evidence by design.
Foundations: repeatable risk management, strong data governance, maintainable documentation, record-keeping, transparency, human oversight, and robust security—plus GenAI/LLM-specific safeguards (prompt/tool hardening, secured integrations, and controls against exfiltration).
AI-TRiSM meets digital sovereignty: control over promises
In AI, sovereignty becomes measurable: you must be able to prove, at any time, who can change the system, what the system is meant to do, how it is secured and operated— and what evidence supports those claims.
- WHO (access & accountability): clear roles, privileged paths, separation of duties, traceable deployments.
- WHAT (model & purpose): defined intended use, boundaries, data/model versions, known failure modes.
- HOW (guardrails & operations): security controls, monitoring, change processes, rollbacks, incident playbooks.
- EVIDENCE (proof): logs, metrics, review artifacts, approvals, tests—reproducible.
No legal buzzwords—just explicit control points that map cleanly to what auditors expect in high-impact and health-adjacent contexts: risk management, data quality, documentation, logging, transparency, human oversight, and strong cybersecurity.
Control points for audit-ready AI
The goal isn’t “more bureaucracy”—it’s fewer surprises. These control points are phrased so you can implement them technically, measure them, and explain them in audits:
Define risks (misclassification, hallucination, bias, data leakage), set acceptance criteria, and run it as a recurring process—not a one-time project.
Data is a security and quality factor: provenance, purpose, representativeness, labeling quality, retention. Without data governance, “monitoring” often becomes optics.
Not “a PDF”, but maintained artifacts: intended use, model/data versions, tests, evaluations, guardrails, dependencies, and a rollback plan.
For meaningful outcomes, you need audit trails: who deployed, which version ran, what inputs were processed (privacy-safe), which guardrails triggered, and what output was delivered.
People must know when AI is involved, what it can and can’t do—and there must be explicit override/review paths for sensitive outcomes.
Protect against prompt injection, insecure tool use, exfiltration, poisoning, and model theft. Practically: input/output validation, least-privilege tooling, secrets protection, isolation, and constrained integrations.
Four building blocks that belong together in operations
1) Explainability & model monitoring
It’s not only about outputs—it’s whether you can explain why, and detect drift, data-quality issues, and bias signals early.
- Human-readable explanations for users and auditors
- Monitoring: performance, drift, data quality, bias signals
- Audit trails for meaningful outcomes
2) ModelOps (reproducible lifecycle)
ModelOps makes AI controllable: versioning, reviews, approvals, controlled rollouts and rollbacks—with clear accountability.
- Versioning: data, features, models
- Approvals & change processes (incl. separation of duties)
- Canary/rollback/retrain as practiced routines
3) AI application security
AI is an application security problem—not only an ML problem. LLMs/agents add specific attack patterns through tools, retrieval, and integrations.
- Hardening prompt/tool chains (least privilege for tools)
- Secrets, connectors, and vector-store security
- Environment isolation (dev/stage/prod) + explicit deploy gates
4) Model privacy
Privacy must be technical: minimization, purpose limitation, safe logging/telemetry, and explicit rules for any training use.
- Minimization & retention (incl. privacy-safe logging)
- On-device / edge where it reduces risk
- No hidden training without an explicit basis
Example: an audit-ready AI pipeline (compact)
- WHO: role model, deployment rights, breakglass, approvals
- WHAT: intended use + boundaries, data/model versions, test catalog
- HOW: guardrails, security controls, canary, monitoring, incident playbooks
- EVIDENCE: logs, metrics, review artifacts, change history, reproducible reports
This turns AI-TRiSM from a “trust label” into operational reality—and fits naturally into a sovereignty approach that makes control measurable.
Coming soon: data controls as the next building block—egress guardrails, data classification as an operational metric, and automated protection paths for storage and keys.