AI inventory & ownership
Document AI tools, models, vendors, business use cases, data types, users, and accountable owners.
Visibility • Ownership • Classification
Responsible AI • Practical governance
Build an AI governance program your organization can actually use.
Establish clear ownership, acceptable-use policy, AI inventory, risk review, human oversight, vendor controls, evidence, and continuous monitoring without turning governance into unnecessary bureaucracy.
No-cost readiness tool
NIST AI RMF aligned approach
Business + technical governance
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Practical governance designed around how your organization actually uses AI.
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Risk-based decisions so higher-risk use cases receive stronger controls.
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Executive visibility with ownership, evidence, risk acceptance, and measurable actions.
Governance building blocks
What an effective AI governance program needs
AI governance should connect policy and accountability to real AI use cases, risk decisions, data handling, third parties, security controls, and ongoing oversight.
Policy & acceptable use
Define approved uses, restricted uses, prohibited data, exception handling, and employee responsibilities.
Policy • Standards • ExceptionsAI risk assessment
Evaluate privacy, security, legal, model, vendor, operational, reputational, and decision-impact risks.
Risk scoring • Review • TreatmentHuman oversight
Define when AI outputs require human review, approval, challenge, or escalation before action.
Human-in-the-loop • Approval • EscalationVendor & data governance
Apply due diligence to third-party AI, contracts, data usage, retention, model training, and subprocessors.
Third party • Data • Contract controlsContinuous monitoring
Track incidents, exceptions, use-case changes, control evidence, review dates, and governance metrics.
Evidence • Metrics • Continuous review
Our approach
Move from AI discovery to sustainable governance
We help you build the program in practical stages so governance grows with your AI adoption.
Discover
Identify current AI use, stakeholders, business drivers, and existing controls.
Classify
Group AI use cases by risk, impact, data sensitivity, and governance requirements.
Design
Define governance roles, policy, review workflows, evidence, and control expectations.
Implement
Operationalize approvals, risk assessment, vendor review, human oversight, and documentation.
Monitor
Measure exceptions, incidents, changes, overdue reviews, and governance performance.
Framework alignment
Align governance with recognized AI risk practices
Your program can be structured around recognized AI governance principles while remaining practical for your size, risk profile, customers, and regulatory environment.
GOVERN
Ownership, accountability, policy, roles, risk appetite, and oversight.
MAPContext, use cases, affected stakeholders, dependencies, and business impact.
MEASURERisk assessment, evidence, control performance, monitoring, and metrics.
MANAGERisk treatment, approvals, remediation, exceptions, and continuous improvement.
AI governance consulting
Turn AI governance principles into an operating model.
Synapse Cyber helps organizations move from policies and framework language to a practical governance program with ownership, risk decisions, approvals, evidence, and recurring oversight.
Governance operating model
Define decision rights, accountable owners, governance committees, escalation, and executive oversight.
AI inventory & risk tiering
Establish a repeatable inventory and classify AI use cases by impact, data sensitivity, autonomy, and business risk.
Policy & human oversight
Set acceptable-use requirements, approval thresholds, human review, exception handling, and documentation standards.
Evidence & monitoring
Define evidence, review cadence, metrics, incidents, changes, and continual governance improvement.
Free interactive tool
See where your AI governance program stands today.
Complete the 10-question readiness checker to get a 0–100 score, maturity level, strongest areas, priority gaps, and recommended next actions.
0–100readiness score
- Ownership & accountability
- Policy & acceptable use
- Risk & data governance
- Human oversight & monitoring
What you leave with
Governance that can be operated, measured, and improved
Governance charter
Clear purpose, scope, accountability, decision rights, and escalation paths.
AI inventory model
A structured way to record AI use cases, tools, vendors, owners, and data exposure.
Risk review workflow
A repeatable process to classify, assess, approve, reject, or remediate AI use.
Policies & standards
Acceptable use, data handling, third-party, human oversight, and documentation expectations.
Control & evidence model
Evidence requirements that support internal governance, customer assurance, and audits.
Improvement roadmap
Prioritized actions with owners, timing, and measurable governance outcomes.
AI governance questions
Common questions organizations ask before starting
What does an AI governance program include?
A practical program typically covers ownership, acceptable use, AI inventory, risk assessment, data handling, third-party AI, human oversight, incident response, monitoring, and periodic review.
Do we need AI governance if we only use commercial AI tools?
Yes. Commercial AI can still create privacy, security, legal, data, vendor, and decision-making risks even when you are not building your own models.
Can AI governance align with NIST AI RMF?
Yes. The program can align with NIST AI RMF while being adapted to your organization’s size, business model, risk profile, and existing cybersecurity or compliance program.
How is AI governance different from AI security?
AI security focuses on protecting systems, models, data, and integrations. AI governance is broader and also addresses accountability, policy, acceptable use, oversight, risk ownership, vendor governance, and lifecycle controls.
Start with the right level of governance
Build AI governance around your real risk—not a generic checklist.
We can help you start with a readiness review or design the complete governance program.
Free AI security tool
Check your AI security readiness in about 4 minutes
Assess identity, data protection, prompt security, vendor risk, integrations, human oversight, logging, incident response, and monitoring.