AI Governance AdvisoryDeploy AI Without the Compliance Headaches
You're moving fast with AI and LLMs, but new risks are emerging that traditional frameworks don't cover. Get a governance program that keeps you compliant with NIST AI RMF and ahead of EU AI Act requirements.
⚡ Your competitors are deploying AI fast. Regulators are catching up faster. The companies that win will be those who build governance into their AI strategy from day one.
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AI Risks Organizations Face Today
Most organizations are adopting AI faster than their governance frameworks can keep up. These are the risks I help clients address.
Shadow AI
Employees using ChatGPT and other AI tools without oversight, potentially exposing sensitive data.
Model Reliability
Hallucinations, bias, and unpredictable outputs that can damage customer trust and create liability.
Regulatory Uncertainty
EU AI Act, state laws, and industry regulations creating a complex compliance landscape.
Third-Party AI Risk
Vendors embedding AI into products without transparency about data handling or model behavior.
Data Privacy
AI systems processing personal data in ways that may violate GDPR, CCPA, or industry regulations.
Board Visibility
Executives and boards struggling to understand AI risks and their fiduciary obligations.
NIST AI Risk Management Framework
My approach is grounded in the NIST AI RMF, providing a structured methodology that's becoming the gold standard for AI governance.
Establish accountability, policies, and oversight structures
- AI governance committee formation
- Roles and responsibilities definition
- Policy framework development
Inventory AI systems and understand context of use
- AI system inventory and classification
- Use case documentation
- Stakeholder impact analysis
Assess and analyze risks across the AI lifecycle
- Risk assessment methodology
- Bias and fairness testing
- Performance monitoring metrics
Prioritize, respond to, and monitor AI risks
- Risk treatment plans
- Incident response procedures
- Continuous improvement processes
Preparing for the EU AI Act
The EU AI Act is the world's first comprehensive AI regulation. Understanding its risk-based approach is essential for any organization deploying AI systems that affect EU citizens.
Unacceptable Risk
AI systems that pose clear threats to safety, livelihoods, or rights. Banned under the EU AI Act.
High Risk
AI systems used in critical areas requiring strict compliance, documentation, and human oversight.
Limited Risk
AI systems with transparency obligations. Users must be informed they are interacting with AI.
Minimal Risk
Most AI applications with no specific requirements, though voluntary codes of conduct encouraged.
Key Deadlines
High-risk AI systems must comply by August 2026. I help organizations assess their AI portfolio, classify systems by risk level, and build compliance roadmaps before deadlines hit.
OWASP Top 10 for LLM Applications
Generative AI introduces novel attack surfaces. I help organizations assess and mitigate risks based on OWASP's authoritative LLM security guidance.
Prompt Injection
Attackers manipulate LLM inputs to override instructions or exfiltrate data.
Insecure Output Handling
Unvalidated LLM outputs passed to downstream systems create XSS, SSRF, or code execution risks.
Training Data Poisoning
Malicious data introduced during training corrupts model behavior.
Model Denial of Service
Resource-intensive queries degrade model performance or availability.
Supply Chain Vulnerabilities
Compromised training data, models, or plugins introduce hidden risks.
Sensitive Information Disclosure
LLMs inadvertently reveal PII, credentials, or proprietary data from training sets.
Insecure Plugin Design
LLM plugins with excessive permissions or insufficient input validation.
Excessive Agency
LLMs granted too much autonomy to take actions without human oversight.
Overreliance
Blind trust in LLM outputs without verification leads to errors and misinformation.
Model Theft
Unauthorized access, extraction, or replication of proprietary LLM models.
Comprehensive AI Governance
A complete program to identify, assess, and manage AI risks while enabling your organization to innovate responsibly.
AI Risk Assessment
Comprehensive inventory and risk classification of AI systems across your organization.
NIST AI RMF Implementation
Governance framework aligned with NIST AI Risk Management Framework principles.
OWASP LLM Security Review
Security assessment against OWASP LLM Top 10 vulnerabilities and attack vectors.
AI Use Policies
Responsible AI policies covering acceptable use, ethics, and procurement guidelines.
Vendor AI Due Diligence
Framework for evaluating AI capabilities in third-party products and services.
Board Education
Executive briefings that translate AI risks into terms boards can act on.
Incident Response for AI
Playbooks for AI-specific incidents like model failures, bias events, or data leakage.
AI Governance Program
Ongoing oversight structure with roles, metrics, and continuous improvement processes.
AI Governance Questions
Common questions about implementing AI governance in your organization.
Actually, now is the ideal time. Establishing governance early is far easier than retrofitting it later. I can help you create a lightweight framework that scales with your AI adoption, preventing the 'shadow AI' problem before it starts.
AI systems introduce unique risks: they can behave unpredictably, exhibit bias, and make decisions that are difficult to explain. Traditional IT governance assumes deterministic systems. AI governance must account for probabilistic outputs, data dependencies, and ethical considerations that IT governance wasn't designed for.
Beyond risk mitigation, strong AI governance enables faster, more confident AI adoption. Organizations with governance frameworks can move from idea to deployment faster because they have clear guardrails. It also protects against regulatory penalties (EU AI Act fines can reach €35M or 7% of global revenue) and reputational damage from AI failures.
Third-party AI is often the biggest blind spot. I help you develop vendor due diligence questionnaires, contractual requirements, and ongoing monitoring processes. Even when you can't control the AI, you can control your exposure to its risks.
Yes. While the EU AI Act is still being finalized, I help organizations prepare by classifying AI systems by risk level, documenting high-risk systems appropriately, and establishing the governance structures the regulation will require. Getting ahead of the curve now avoids scrambling later.
A foundational program can be established in 2-3 months, including risk assessment, core policies, and governance structure. A comprehensive program with full NIST AI RMF alignment typically takes 6-12 months, depending on the complexity and number of AI systems in your environment.
Client Success Stories
See how organizations like yours achieved measurable outcomes.
Ready to Govern AI Responsibly?
Let's discuss your AI adoption journey and build a governance framework that enables innovation while managing risks.
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