NIST AI Risk Management Framework 1.0
AI security best practices, made operational
The NIST AI RMF gives enterprises a common language for trustworthy AI: four functions — GOVERN, MAP, MEASURE, MANAGE — applied across the AI lifecycle. Below, BEARACH translates the framework into a concrete Do's & Don'ts matrix for Canadian businesses, covering data privacy, model governance, PIPEDA/PHIPA, shadow AI, and Zero Trust AI architecture. Filter by topic or signal.
Reference: NIST AI RMF 1.0 (NIST AI 100-1, January 2023) and the NIST Generative AI Profile (NIST AI 600-1). This guidance supports — but does not replace — advice from your legal counsel.
Establish an AI governance structure: accountable executives, acceptable-use policy, risk tolerance, and an inventory of every AI system in the business.
Map each AI use case to its context: what data it touches, who it affects, applicable Ontario/Canadian regulation, and where the failure modes live.
Measure model behaviour continuously: accuracy, drift, bias, prompt-injection resilience, and privacy leakage — with metrics reviewed on a schedule.
Manage residual risk: incident response for AI failures, human-in-the-loop controls, vendor exit plans, and documented decommissioning.
Classify data before it ever reaches a model
Tag customer PII, PHI, and trade secrets at ingestion. Only pre-approved data classes may flow to external AI APIs; sensitive classes stay on Canadian-resident or on-prem systems.
Never paste client PII into consumer AI tools
Free consumer chatbots may retain and train on inputs. Under PIPEDA's accountability principle, transferring personal information to a processor without contractual safeguards and transparency about the transfer puts you offside.
Log and review AI prompts that touch personal data
Prompt logs are themselves personal-information records. Retain them under your existing retention schedule, encrypt them, and audit access quarterly.
Maintain a living AI system inventory
Every model, agent, and AI-enabled SaaS in the business is registered with an owner, purpose, data classes, and risk rating — the foundation NIST AI RMF's GOVERN function expects.
Evaluate models before and after deployment
Baseline accuracy, bias, and safety behaviour pre-launch, then re-test on a schedule and after every model/version swap. Keep evaluation evidence for auditors and insurers.
Don't let agents act without bounded permissions
Autonomous agents need scoped credentials, spend limits, approval gates for irreversible actions, and full action logs. An agent with your admin token is an unbounded liability.
Track Ontario Bill 194 and the shifting federal landscape
Ontario's Bill 194 now governs AI use in the public sector — it shapes procurement expectations for anyone selling into government. Federally, AIDA (Bill C-27) died in 2025; PIPEDA remains the operative private-sector law, with Quebec's Law 25 adding automated-decision rules if you serve Quebec customers.
Keep regulated data resident in Canada where required
PHIPA custodians and many public-sector contracts expect Canadian data residency. Choose Canadian regions from your cloud/AI vendors, or deploy local models on-prem in Ontario.
Update privacy policies to disclose AI processing
If AI systems make or support decisions about individuals, your notices should say so in plain language, and meaningful human review must be available for significant decisions.
Don't sign AI vendor contracts without data terms
Reject vendors who won't commit in writing to no-training-on-your-data, breach notification timelines, deletion on exit, and disclosure of subprocessors and hosting regions.
Offer a sanctioned AI path so staff don't improvise
Shadow AI thrives where official tools lag. Provide an approved, capable assistant with SSO and DLP, plus a fast intake process for new AI tool requests.
Monitor for unsanctioned AI usage on the network
Use CASB/DNS telemetry to detect unapproved AI endpoints. Treat discoveries as process signals to triage, not grounds for punishment — then fold real needs into the sanctioned stack.
Don't ignore browser extensions and 'AI features' in SaaS
Vendors quietly enable AI features that exfiltrate content for processing. Review SaaS AI settings, disable by default, and re-enable only after privacy review.
Apply least-privilege and segmentation to AI workloads
Treat every model endpoint and agent as untrusted by default: authenticate every call, isolate inference networks, and gate tool access through policy-enforcing proxies.
Red-team for prompt injection and data exfiltration
Test RAG pipelines and agents against indirect prompt injection, tool abuse, and secret leakage before go-live and after major changes. Document findings and fixes.
Never expose internal RAG indexes without access controls
A vector store of company documents is a crown-jewel dataset. Enforce document-level permissions at retrieval time — not just at the app layer — or private data will surface in answers.
Want this matrix applied to your organization?
The BEARACH AI Governance Audit inventories every AI system in your business, scores it against the NIST AI RMF, and delivers a prioritized remediation roadmap with PIPEDA/PHIPA mapping.
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