01
Clear accountability
Define who owns each AI system, who accepts risk, who approves release, and who responds when performance changes.
AI governance & assurance
Govern AI. Keep moving.
We turn responsible AI principles, risk frameworks, and regulatory obligations into controls that product, engineering, risk, and business teams can actually operate.
Operational governance / 01
Governance should improve decisions, not create a parallel bureaucracy.
Effective AI governance makes ownership clear, applies controls in proportion to risk, preserves evidence, and continues after launch. We connect policy to the product lifecycle so teams know what must be assessed, approved, monitored, and escalated.
02
Proportionate controls
Classify use cases by impact and apply review, testing, documentation, and oversight that match their actual risk.
03
Evidence that stays current
Create traceable records of purpose, data, evaluation, decisions, limitations, incidents, and material changes across the lifecycle.
Governance capabilities / 02
From AI inventory to production oversight.
Our work can align with established frameworks such as the NIST AI RMF and ISO/IEC 42001, while remaining specific to your systems, roles, and regulatory context.
/01
AI inventory & classification
Establish what AI systems exist, where they are used, who owns them, which third parties are involved, and how material each use case is.
- System inventory
- Use-case classification
- Ownership mapping
- Third-party register
/02
Governance operating model
Define decision rights, forums, policies, standards, and escalation routes across business, product, technology, risk, legal, and compliance teams.
- Roles and accountability
- Policies and standards
- Approval pathways
- Escalation design
/03
AI risk assessment
Assess foreseeable harm and operational exposure across data, privacy, security, bias, explainability, robustness, misuse, and dependency risk.
- Impact assessment
- Risk taxonomy
- Control mapping
- Residual risk decisions
/04
Evaluation & release controls
Translate intended purpose and risk into representative tests, thresholds, adversarial review, and evidence-based release decisions.
- Evaluation design
- Robustness testing
- Red teaming
- Release gates
/05
Human oversight
Design meaningful review, intervention, appeal, and fallback mechanisms for decisions where people remain accountable.
- Review points
- Override controls
- User disclosure
- Appeal and recourse
/06
Monitoring & assurance
Track performance, incidents, changes, and control effectiveness after deployment, with evidence suitable for internal and external review.
- Production monitoring
- Incident process
- Change assessment
- Assurance evidence
How we deliver / 03
Controls built into delivery.
Governance works best when it follows the same lifecycle as the product, from intended purpose through operation and retirement.
01
Map
Inventory systems, owners, users, third parties, data, decisions, and applicable obligations.
02
Assess
Classify impact, identify risks, test existing controls, and agree the level of assurance required.
03
Control
Embed approvals, evaluations, documentation, oversight, security, and fallback behavior into delivery.
04
Monitor
Review performance and incidents, assess material changes, and maintain evidence over time.
Questions / 04
Practical AI governance questions.
01What is AI governance?
AI governance is the set of roles, policies, decision processes, controls, and evidence used to direct and oversee how an organization develops, procures, deploys, and monitors AI systems.
02How is AI governance different from model governance?
Model governance focuses mainly on models. AI governance covers the wider system and context: intended use, users, data, interfaces, third parties, automated actions, human oversight, security, monitoring, and organizational accountability.
03Can you help with the EU AI Act?
We can help organizations inventory and classify use cases, map operational obligations, design controls, and prepare technical evidence. Legal interpretation and formal compliance opinions should remain with qualified legal counsel.
04Does AI governance slow product teams down?
Poorly designed governance can. Proportionate governance should make expectations and decision rights clear early, reducing late rework and helping teams move lower-risk use cases through a lighter path.
05What evidence should an AI system retain?
The evidence depends on risk and context, but commonly includes intended purpose, ownership, data provenance, model and vendor details, evaluation results, known limitations, approvals, user information, monitoring records, incidents, and material changes.
Start with the real problem
Make governance part of how AI gets built.
Tell us where AI is already in use, which decisions carry the most consequence, and where ownership or evidence is unclear. We will help turn that gap into an operating system.
Talk to KeshoLet's build / 05