For compliance, risk, and assurance teams
Make responsible AI operational.
Policies and principles become useful when they shape how AI is selected, designed, used, and monitored. I help compliance and risk teams translate them into clear accountability, workable controls, and evidence.
What responsible AI needs in practice
A defined use and clear boundaries
What is the AI intended to do, and which decisions or actions must remain outside its scope?
Named accountability
Who owns the use case, reviews its operation, challenges it, and responds when something goes wrong?
Proportionate controls
What human review, permissions, limits, testing, and escalation paths are appropriate for the risk?
Evidence and traceability
Can the organization reconstruct what happened, which information was used, and who reviewed or changed the result?
Monitoring and response
How will performance, misuse, unexpected behaviour, provider changes, and incidents be detected and handled?
Could you reconstruct one AI-supported decision six months later?
Could you show which system and version ran, what information it used, what it produced, who reviewed it, and what changed before the result was used?
Responsible AI is not demonstrated by a policy alone. It is demonstrated by what the organization can explain, evidence, monitor, challenge, and stop.
What I help teams put in place
- An AI governance model
- Clear roles, decision rights, review responsibilities, escalation paths, and accountability.
- Use-case governance
- A practical approach to inventorying, assessing, classifying, approving, and revisiting AI uses.
- Operational controls and evidence
- Workflow-level controls, human oversight, documentation, monitoring, change management, incident response, and stopping conditions.
AI does not arrive on its own
AI-specific rules are only one part of the picture. Introducing AI may also affect existing responsibilities across privacy, security, third parties, operational resilience, employment, consumer protection, and sector regulation.
The useful question is not only, “Which rules mention AI?” It is, “What changes in this organization and workflow once AI is introduced?”
Ways to work together
Engagements can start with one governance workshop or extend into ongoing advisory and hands-on operationalization support.
Governance workshops
Examine one use case or governance challenge, align the relevant teams, and identify the decisions, risks, controls, and evidence that need attention.
Ongoing advisory
Support compliance and risk leaders as they develop governance models, assessment practices, policies, oversight, and executive reporting.
Operationalization support
Work with business and technology teams to translate governance requirements into workflows, controls, evidence, monitoring, and response processes.
This work does not replace legal advice, regulatory interpretation, or formal internal approval. It gives the responsible functions a clearer and more operational basis for their work.
Prefer email? Write to hossein@initialblock.com.
Relevant experience
- Nine years building operational infrastructure for financial firms
- Experience translating regulatory and operational requirements into working systems
- More than fifteen years building technology
- MSc in Artificial Intelligence from the University of Amsterdam
This combination helps me work between compliance, risk, business, and technology, where AI governance ultimately has to function.
Book a free call
Bring one AI use case or governance challenge.
In a free 15-minute call, we can identify where the uncertainty lies and whether a workshop, advisory engagement, or hands-on support would be useful.