Govern AI responsibly. Reduce risk. Prove compliance.
We help you design, implement, and evidence AI that is safe, fair, explainable, and secure aligned to the EU AI Act, NIST AI RMF, ISO/IEC 42001 (AI management), ISO/IEC 23894 (AI risk), and privacy laws like DPDP Act. and GDPR.
Why it matters
- Cut regulatory risk (EU AI Act, DPDP, GDPR) with audit-ready controls
- Ship AI faster, safer with clear guardrails and approvals
- Win enterprise deals by proving governance, security, and fairness
What’s included (business + technical)
1) Program & Policy (Governance)
- AI governance framework, RACI, ethics board, risk taxonomy, model approval gates
- Responsible AI policy (fairness, transparency, safety, human oversight, accessibility)
2) Regulatory & Privacy Mapping
- EU AI Act risk classification (limited/high risk) and obligations register
- Privacy by design: DPIAs, lawful basis, purpose limitation, minimization, retention
3) Model & Data Lifecycle Controls
- Inventory & Risk: Model registry, use-case register, risk scoring (NIST/ISO aligned)
- Data Governance: Lineage, consent/provenance, de-identification, anonymization/pseudonymization, synthetic data controls
- Secure MLOps/LLMOps: Versioning, CI/CD gates, change control, SoD, secrets management
- Testing & Evaluations: fairness/bias, robustness/adversarial, safety/toxicity, privacy leakage, hallucination rate, guardrail policies
- Red Teaming: prompt-injection/jailbreak tests, abuse scenarios, safety baselines
- Explainability & Transparency: XAI methods, model cards & system cards, datasheets for datasets, user disclosures
- Human-in-the-Loop: review/override for sensitive decisions; contestability/appeals
- Monitoring & Incidents: drift/safety/fairness dashboards, abuse detection, rollback plans, AI incident register
- Security & Access: API controls, key management, rate limiting, content filtering, secure prompt handling
- Third-Party & Cloud: Vendor due diligence (AI/LLM SaaS), DPAs, cross-border transfer risk, data residency/sovereignty plans (incl. CLOUD Act considerations)
4) Change Management
- Role-based training for product, data, legal, and leadership
- Playbooks/runbooks, communications & adoption plan
Responsible AI (RAI) — baked in
Principles → Controls → Evidence
- Principles: fairness, accountability, transparency, privacy, safety, human oversight, inclusion, sustainability
- Operational controls: ethics review gates, bias testing & mitigation thresholds, explanation UX, appeals/grievance SOPs, safety red-team reports, accessibility checks
- Evidence: RAI policy & charter, RAI impact assessments, model/datasheet documentation, fairness & safety test results
Deliverables you receive
- AI Governance Framework & Policy Pack
- Model Inventory, Use-Case Register & Risk Register
- AI Impact Assessments (EU AI Act-aligned) and DPIAs (DPDP/GDPR)
- Testing & Red-Team Reports, evaluation scorecards (fairness/robustness/safety)
- Monitoring Dashboards (drift, safety, quality) & Incident Response Playbooks
- Compliance Evidence Pack (model cards, datasheets, approvals, logs)
Who it’s for
- Organizations building or integrating AI/LLM (apps, chatbots, decisioning)
- Teams needing audit-ready AI controls for enterprise clients or regulators
- Companies operating across regions that require privacy & sovereignty assurance
Practical guardrails, clear evidence for audits, and faster, safer AI delivery—without slowing innovation.
Contact Us to set up a discovery workshop and receive a tailored AI governance roadmap.