Most organizations are deploying AI faster than their governance can keep up. Employees are using ChatGPT with customer data. Teams are building automations without security review. Nobody knows what data is going where or who has access to what.
AI governance isn't about saying no to AI. It's about saying yes responsibly. Clear policies, appropriate controls, and practical guardrails that let you move fast without creating risk.
What We Address
Access Control
Who can use which AI systems with what data? Role-based access, authentication requirements, and approval workflows.
Data Boundaries
What data can be processed by AI, where, and under what conditions? Classification, handling rules, and residency requirements.
Audit & Logging
Complete visibility into AI usage: who used what, when, with what data, and what decisions were made.
Human Oversight
Which decisions require human review? Escalation paths, approval thresholds, and override procedures.
Vendor Assessment
Evaluating AI vendors and tools for security, privacy, and compliance before deployment.
Incident Response
What happens when AI goes wrong? Procedures for errors, data exposure, and system failures.
Our Approach
1. Current State Assessment
We audit your current AI usage: sanctioned and shadow. What tools are people using? What data is being processed? What controls exist today?
2. Risk Analysis
We identify the specific risks your organization faces: data types, regulatory requirements, industry standards, contractual obligations, and reputational considerations.
3. Policy Development
We create practical, implementable policies that your team can actually follow. Not 50-page documents nobody reads. Clear, specific guidance for real situations.
4. Control Implementation
We help implement technical controls: access restrictions, monitoring, logging, and enforcement mechanisms that make policies stick.
5. Training & Rollout
We train your team on the new policies and help you communicate changes to the organization in ways that build buy-in rather than resistance.
Governance Framework Components
- AI Acceptable Use Policy — What's allowed, what requires approval, what's prohibited
- Data Classification for AI — Which data categories can be used with which AI systems
- Vendor Evaluation Checklist — Security and privacy criteria for AI tool selection
- Human Review Requirements — When AI decisions require human oversight
- Audit Log Requirements — What to log, how long to retain, who can access
- Incident Response Procedures — How to handle AI errors and data incidents
- Training Requirements — What employees need to know before using AI tools
- Exception Process — How to request exceptions and who approves
Compliance Context
AI governance often intersects with existing compliance requirements:
- HIPAA — Healthcare data processing with AI requires specific safeguards
- SOC 2 — AI systems become part of your security posture
- GDPR/CCPA — Automated decision-making and data subject rights
- Industry regulations — Financial services, insurance, government contractors
- Contractual obligations — Customer and vendor agreements about data handling
We work within your existing compliance framework, adding AI-specific controls that align with your current obligations.
Shellproof Partnership
For organizations with heightened security requirements, including defense contractors and those pursuing CMMC compliance, we partner with Shellproof Security, a CMMC-certified assessor organization. This allows us to provide AI governance that meets the most stringent federal security standards.
Investment
AI governance engagements typically range from $8,000 for focused policy development to $25,000+ for comprehensive governance programs including implementation support and training. We scope based on organizational complexity, regulatory requirements, and current maturity.
Need to get AI governance right?
Let's talk about your situation and what level of governance makes sense.
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