Cognitive View

Open Cognitive View and begin by building a live catalog of every AI system your teams touch. Pull in internal models, vendor tools, and anything discovered outside official channels, then tag each item with owner, purpose, data sensitivity, business unit, and region. Flag unapproved tools for triage, merge duplicates, and set lifecycle states (proposed, in use, retired) so everyone knows what exists and who’s responsible.

Next, assess and prioritize. Launch a risk review using templates aligned with NIST AI RMF, the EU AI Act, and ISO/IEC 42001. The platform produces a clear rating and visual risk map across use cases, models, and departments. Route high-impact items to accountable owners, pick a treatment plan (mitigate, accept, monitor, deprecate), and record guardrails such as data use limits, human oversight steps, and testing criteria. Controls are mapped to the relevant articles and categories from global frameworks, and you can attach evidence like policies, DPIAs, test reports, and vendor assurances directly to each AI asset.

Operationalize governance with a visual flow designer. Define intake forms, review checkpoints, and sign‑off stages without writing code. Set thresholds that automatically escalate high‑risk use cases to legal, security, or compliance; auto‑assign reviewers by domain; and require approvals before deployment or scale‑up. As models change, trigger re‑assessments and drift checks. All artifacts—decisions, model logs, policy coverage, and change history—are captured in an evidence vault. Use dashboards to filter by region, business unit, or model type, monitor backlogs and SLA timing, and export audit‑ready reports for internal committees or external regulators.

When you’re ready to show your work, publish a tailored Trust Center. Choose what to share by audience—regulators, customers, partners, or employees—such as your governance scope, control mappings, risk classifications, and key documents. Keep it current without manual rework; updates flow from your source records. Finally, set the automation level that fits your culture: assistant‑guided suggestions for reviewers, human‑in‑the‑loop gates for sensitive steps, or hands‑off execution for routine tasks. Schedule periodic recertifications, watch third‑party tools for changes, and receive alerts when risks cross a threshold. With these workflows in place, teams ship AI features faster while staying within policy and ready for scrutiny at any time.