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Pyagna provides focused consulting for organizations building AI governance that works in practice. Offerings reflect how mature programs are structured—aligned with the NIST AI RMF (Govern, Map, Measure, Manage) and with ISO/IEC 42001-style AI management systems and impact assessments—so you can move from policy to operational discipline.

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Executive sponsorship & governance operating model

Establish the authority and cross-functional structure AI governance needs. Effective programs are company-wide initiatives with clear sponsorship so governance is not perceived only as a barrier to delivery.

What it includes

  • Design for senior executive sponsorship and visible leadership support
  • Stakeholder mapping across Legal, Security, Engineering, Product, Marketing, and other domains
  • Governance charter and oversight model aligned to the “Govern” function (e.g., NIST AI RMF)
  • Operating cadence for committees or steering groups that own AI governance decisions

Deliverables

  • Governance charter and role definitions
  • Executive-ready sponsorship and stakeholder plan
  • Committee / oversight model and decision rights

GRC integration & organizational fit

Integrate AI governance with existing Governance, Risk, and Compliance (GRC) structures instead of duplicating them. Map current processes and owners so AI governance augments what already works.

What it includes

  • Inventory of relevant GRC, security, and compliance processes
  • Workshops with security, legal, compliance, engineering, and product stakeholders
  • Integration paths for AI-specific controls within existing risk and compliance workflows
  • Day-to-day partnership model between central governance and delivery teams

Deliverables

  • Integration assessment and gap summary
  • Recommended operating model within your GRC landscape
  • RACI or handoff model for AI governance activities

Program scope & AI risk strategy

Define what the program covers (product AI features, internal AI use, models vs. features) and which risks you will track. Scope and risk taxonomy choices determine how deep and wide assessments go.

What it includes

  • Scope decisions: product vs. internal use, systems vs. models, geographic or business unit boundaries
  • Alignment to stakeholder roles (e.g., ISO/IEC 22989 concepts) where useful
  • Selection of risk categories to track beyond pure security (compliance, transparency, fairness, operational, etc.)
  • Practical risk taxonomy tailored to your industry—not a one-size-fits-all academic list

Deliverables

  • Written scope statement and boundaries
  • AI risk taxonomy and applicability matrix
  • Prioritized risk focus areas for mapping and measurement

AI system inventory, mapping & impact assessments

Build the “Map” layer: inventory AI systems in scope, collect structured context, and run qualitative AI risk or impact assessments (consistent with ISO 42001-style AI impact assessments). Prefer structured questionnaires over unstructured document piles.

What it includes

  • AI system / use-case inventory and ownership
  • Qualitative assessment design (questionnaires) for context: users, data flows, accountability, documentation, legal/compliance, security, third parties
  • Mapping systems to in-scope risks and recording presence or absence of risk factors
  • Third-party and vendor AI review patterns (e.g., standardized supplier questionnaires where appropriate)
  • Coordination with tooling choices where you already use governance or GRC platforms

Deliverables

  • System inventory and risk mapping artifacts
  • Assessment questionnaire templates and scoring or threshold guidance
  • AI impact assessment summaries per system or use case

AI risk measurement & quantitative assessment

Support the “Measure” function: where qualitative mapping shows material risk, define quantitative or empirical tests (e.g., bias, performance, security) appropriate to the risk and system maturity.

What it includes

  • Prioritization of which risks warrant quantitative measurement
  • Measurement methodology selection (metrics, tests, sampling, monitoring hooks)
  • Alignment of measurement cadence to product and model lifecycle
  • Practical difficulty triage—quantitative assessment is often the hardest part of a mature program

Deliverables

  • Measurement plan per prioritized risk
  • Metric definitions and acceptance thresholds where applicable
  • Recommendations for monitoring and evidence retention

Ongoing risk management & lifecycle maintenance

Operationalize the “Manage” function: treat AI risk management as continuous. Mitigate, treat, escalate, and maintain oversight through the full lifecycle of each AI system—not a one-time project.

What it includes

  • Risk treatment workflows: mitigation, acceptance, transfer, avoidance
  • Lifecycle integration: design, change, release, retirement
  • Incident and issue management hooks for AI-specific events
  • Governance cadence so the program does not lose steam after launch

Deliverables

  • Risk treatment and escalation playbooks
  • Lifecycle checkpoints for governance review
  • Maintenance and audit cadence recommendations

ISO 42001 readiness & alignment with ISO 27001

Prepare for ISO/IEC 42001 (AI management system) certification or structured external assurance. Programs that already align with NIST AI RMF are much of the way toward ISO 42001; ISO 27001-certified organizations can often coordinate controls and evidence efficiently.

What it includes

  • Gap analysis against ISO 42001 themes (e.g., leadership, planning, support, operation, performance evaluation, improvement)
  • Coordination with information security and compliance teams on overlapping evidence (especially when ISO 27001 is in place)
  • Coverage of areas that generic frameworks may under-specify (e.g., AI-specific incidents, training, continual improvement)
  • Roadmap to audit readiness without unnecessary documentation theater

Deliverables

  • ISO 42001 gap analysis and prioritized roadmap
  • Control and evidence mapping suggestions (including ISO 27001 touchpoints where relevant)
  • Certification readiness checklist

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