Govern AI With Confidence.
Build Responsible AI at Scale.
Establish governance, accountability and oversight across AI systems, use cases and lifecycle decisions with one connected AI governance workspace.
Request a DemoMap AI Governance to the Standards That Matter
Connect AI governance activity to the regulations and frameworks shaping responsible AI, from emerging AI-specific standards to the security and privacy frameworks your organization already follows.
ISO/IEC 42001Structure AI management system controls around ISO/IEC 42001's governance, risk and lifecycle requirements.
One Workspace for Responsible AI
When every team tracks its own AI systems in its own spreadsheet, nobody can answer "which models touch regulated data?" on the day it matters. Cytrusst gives every AI system, owner and decision one governed record instead of ad hoc tracking.
| AI System | Owner | Risk Tier | Status |
|---|---|---|---|
| Customer Support Bot | Product Team | High | Compliant |
| Fraud Detection Model | Risk Team | Medium | Under Review |
| HR Screening Model | HR Team | High | Action Required |
| Code Assistant | Engineering | Low | Compliant |
See every AI system, model and use case deployed across the organization.
Apply consistent governance policies across every AI initiative.
Assign clear ownership for every AI system and the decisions it drives.
Apply the right level of scrutiny based on each system's risk profile.
Translate AI policy into controls that are actually enforced in practice.
Give leadership a real-time view of AI governance posture.
Know Every AI System in Use
Maintain a complete, current inventory of AI systems, models, use cases and the data and vendors behind them — the same register the team pulls up before every governance review.
| AI System | Model | Owner | Status |
|---|---|---|---|
| Customer Support Bot | GPT-4 | Product Team | Active |
| Fraud Detection Model | XGBoost | Risk Team | Active |
| Marketing Content Gen | Claude | Marketing | Active |
| HR Screening Model | Custom LLM | HR Team | Review |
Maintain a single, current register of every AI system in use.
Complete visibilitySurface AI use cases across business units, including shadow AI.
Find all use casesTrack the models, providers and vendors behind every AI system.
Map dependenciesConnect AI systems to the data sources that feed and train them.
Trace data lineageTrack model versions and configuration changes over time.
Monitor changesIdentify who owns, operates and is affected by each AI system.
Assign accountabilityUnderstand Risk Before It Becomes Exposure
Assess AI systems for risk, score and prioritize what needs attention, and define the mitigations required before deployment.
Identify risks specific to model behavior, bias and misuse.
Assess potential impact on individuals, decisions and outcomes.
Score AI risk consistently across systems and use cases.
Model behavior and safety
Data and privacy risks
Bias and fairness
Security and resilience
Regulatory and ethical impact
Operational and business risk
Focus governance attention on the highest-risk AI systems first.
Define safeguards required before an AI system can proceed.
Reassess AI risk as models, data and usage evolve.
| Policy | Controls | Status |
|---|---|---|
| Responsible AI Policy | 12 | Active |
| Model Use Policy | 8 | Active |
| Data Governance Policy | 10 | Under Review |
| AI Ethics Policy | 9 | Draft |
Turn AI Policy Into Enforced Controls
Maintain AI policies and translate them into controls, guardrails and approval workflows that are actually followed, with an accountable owner behind every one.
Maintain acceptable-use, model-risk and data policies in one place.
Map AI controls to the policies and frameworks that require them.
Enforce guardrails for fairness, transparency and human oversight.
Route new AI use cases through structured review and sign-off.
Track and time-box exceptions to standard AI policy.
Assign accountable owners to every AI control in place.
Govern AI From Proposal to Retirement
Apply stage-gated governance across the full AI lifecycle, with sign-off, change control and an audit trail at every stage — including the model and configuration changes a team reviews together before they ship.
| Stage | Status |
|---|---|
| Proposal | Approved |
| Model Review | In Progress |
| Build & Deploy | Pending |
| Monitor & Retire | Pending |
Track AI systems from proposal through retirement.
Require governance sign-off at each lifecycle milestone.
Monitor model drift and performance against approved baselines.
Govern retraining, fine-tuning and configuration changes.
Retire AI systems in a controlled, auditable way.
Preserve a defensible record across the full AI lifecycle.
Stay Ready for AI Regulation
Map AI systems to applicable regulations, collect supporting evidence and keep every assessment audit-ready — before a regulator or customer asks for it, not after.
| Framework | Coverage | Status |
|---|---|---|
| EU AI Act | 92% | On Track |
| NIST AI RMF | 88% | On Track |
| ISO/IEC 42001 | 76% | Gaps Found |
Map AI systems to the regulations and standards that apply to them.
Centralize the evidence that demonstrates AI compliance.
Maintain a complete record of every AI compliance assessment.
Track compliance posture across systems and frameworks.
Produce reports ready for internal or regulatory review.
Track gaps identified during assessment through to closure.
Keep Watching After AI Goes Live
Approval is a moment; drift, bias and misuse happen over months in production. Monitor deployed AI systems continuously, keep humans in the loop at the decisions that need them, and route issues to the right owner with full context attached.
Monitor AI systems in production for drift, bias and anomalies.
Surface AI incidents before they escalate into business issues.
Give oversight teams a real-time view of AI system health.
Keep humans in the loop at the decisions that require them.
Route AI issues to the right owner with full context attached.
Keep monitoring evidence current for continuous assurance.
Review the system behind the model.
An AI model is only one part of an AI system. Governance also needs its intended use, data sources, affected users and the controls that support a deployment decision.
- Intended use
- Draft support responses for human review
- Model & provider
- Approved model version and vendor record
- Data boundary
- Approved support knowledge; personal-data handling reviewed
- Human oversight
- Support agent checks each response before sending
- Release evidence
- Risk assessment, control checks and accountable approval
- Reassessment trigger
- Model change, new data source or expanded use
Carry AI oversight beyond the release decision
Follow an AI use case from inventory and impact review to approved controls, production oversight and reassessment.
Find every AI system and use case in the organization.
Evaluate risk, impact and regulatory exposure.
Apply policy, ownership and approval workflows.
Enforce guardrails and safeguards for responsible AI.
Track AI systems continuously in production.
Maintain evidence ready for audit and regulation.
Govern AI with confidence before scale outpaces oversight.
Explore AI GovernanceDiscover AI. Govern Confidently. Prove Continuously.