Strategic GRC & Security provides short-term, scoped advisory support for complex AI governance, cybersecurity, GRC, and system-control challenges.

System architecture is what controls AI consequence.

Engagements are designed to help organizations clarify the problem, evaluate system behavior, identify governance intents and controls, and produce usable outputs for leadership, security, engineering, compliance, and audit teams.

Advisory Engagements May Include:

These engagements are not long implementation projects or generic compliance checklists. They are structured to answer a defined question, support a specific decision, or help a team move from ambiguity to action.

  • A short assessment of whether an AI-enabled workflow has defined authority, bounded actions, controlled context, enforceable decision points, and evidence.

  • A working session with security, architecture, legal, and GRC to identify consequential actions and define admissibility boundaries.

  • For organizations needing customer/regulator defensibility: what can they actually prove about AI use, authority, approvals, logging, and control?

  • A scoped engagement to create AI risk register items and quickly map them to NIST 800-53, ISO, SOC 2, or internal control structures using my Control Intent Engine.

  • Preparation for audits, client questions, regulatory scrutiny, or internal governance reviews

Flowchart illustrating a process from system reality to decision evidence, including steps such as governance intent and framework mapping, with references to architecture, behavior, data flows, automation, and standards like NIST, ISO, and SOC 2.

Intent-Driven Governance

Governance starts with how the system actually works, then determines which intents, controls, and decision evidence are required.

What it solves:

AI governance ambiguity Framework fatigue

Agent and tool risk Audit & regulatory uncertainty

Non-human identity sprawl Governance drift

Advisory Services

Short, scoped advisory services structured around the same principles: start with system reality, clarify the governance intent, and translate that into practical controls, evidence, and leadership-ready decisions.

Intent-based AI governance design

Clear limits on what AI systems can access and do

Regulator-ready governance models

AI Governance Architecture

AI risk in loss-exposure terms

ERM-aligned reporting

Board-level narratives

Risk Translation & Executive Reporting

Control Intent Engine–based assessments

System facts to governance

ISO / NIST / SOC 2 projection

Control Intent & Governance Automation

Pre-audit / pre-regulatory readiness

Product launch & M&A review

Partner advisory support

Readiness & Advisory

Common engagement examples:

• Evaluate an AI-enabled workflow for admissibility, authority, context, actions, and evidence
• Translate AI governance requirements into system-level control expectations
• Review whether a proposed AI use case needs run, check, or gate treatment
• Help a security or GRC team prepare for executive discussion of AI risk
• Map system facts to applicable NIST, ISO, SOC 2, or internal control expectations
• Support a consulting or vCISO team on complex AI governance questions

Ready to Talk?

Tell me where governance, risk, or system control is challenging and I’ll help you find a practical path forward.