Readiness Process

Sprint Timeline

The engagement follows structured phases, each building on the outputs of the previous one.

1

Intake

2–6 days
  • NDA & stakeholder map
  • Document request
  • Scoping interviews
  • System boundary draft
2

Assessment

9 days
  • TSC selection
  • Type 1/Type 2 recommendation
  • Control walkthroughs
  • Evidence sampling
3

Outputs

9 days
  • Controls matrix & gap register
  • Policy/document backlog
  • Evidence calendar
  • Executive readout & roadmap
4

Follow-on

Variable
  • Remediation implementation
  • Type 2 observation period

Phase Details

1. Intake & Scoping Week 1

We start by understanding your AI landscape and current governance posture.

  • AI system inventory — catalog all AI systems in development and production, including purpose, data sources, and deployment context
  • Current governance practices review — assess existing policies, processes, and oversight mechanisms for AI development
  • Regulatory applicability analysis — determine which frameworks apply (EU AI Act, NIST AI RMF, state laws, sector-specific requirements)
  • Stakeholder mapping — identify AI developers, deployers, risk owners, and affected populations

2. Assessment Week 2–3

We evaluate your AI systems and practices against governance framework requirements.

  • Risk categorization of AI systems — classify each system by risk level using applicable framework criteria
  • Bias and fairness evaluation — assess current testing practices and identify gaps in bias detection and mitigation
  • Transparency and explainability assessment — evaluate whether AI decisions are documented and explainable to affected individuals
  • Human oversight mechanism review — assess whether appropriate human review and intervention mechanisms exist

3. Outputs Week 3–4

We deliver the artifacts that establish your AI governance framework.

  • AI governance framework — policies, roles, processes, and oversight structure tailored to your organization
  • Risk categorization matrix — every AI system classified with risk level, rationale, and required controls
  • Bias testing methodology — defined approach to testing for bias and fairness in high-risk AI systems
  • Transparency documentation templates — standardized formats for documenting AI decisions, data sources, and model behavior

4. Follow-on Ongoing

After the readiness sprint, ongoing activities keep your governance program current.

  • Model monitoring program — continuous monitoring of AI system performance, drift, and fairness metrics
  • Regulatory change tracking — monitor evolving AI regulations and update governance practices accordingly
  • Annual governance review — periodic assessment of governance framework effectiveness and completeness

Sprint Deliverables

Every readiness sprint produces these minimum deliverables:

AI system inventory
Risk categorization matrix
AI governance framework
Bias testing methodology
Transparency documentation templates
Human oversight mechanisms
Data governance controls
Regulatory applicability analysis

Start Your Readiness Sprint

Most companies complete the readiness sprint in 3–4 weeks. The result is a clear AI governance framework aligned to NIST AI RMF, EU AI Act, and industry best practices.

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