AI Will Move Fast. Governance Must Move First.
Average cost of a data breach
Of organizations lack formal AI governance policies
For an AI agent to destroy 3 months of company data
Reported major operational disruptions
The Challenge
Most organizations are deploying AI fast than they are governing it.
Artificial intelligence is rapidly becoming embedded in everyday business processes. But as AI adoption accelerates, many organizations are still determining who owns AI risk, what employees can and cannot do with AI, how AI vendors should be evaluated, and whether technical safeguards are sufficient.
Effective AI security requires more than a policy. It requires clear accountability, defined controls, ongoing risk management, and technical testing. Understanding and managing AI security risks is an important first step toward building a secure and sustainable AI strategy. Organizations looking to evaluate their broader cybersecurity posture can also explore Dean Dorton’s Cybersecurity Scorecard.
The Service
AI Security Governance Program
Executives are asking:
- Who owns AI risk and who is accountable?
- What AI use is acceptable for employees?
- How do we assess third-party AI vendors?
- How do we evaluate model risk and data leakage?
- Is our infrastructure configured to prevent autonomous AI actions?
What makes this different:
- Executive-level governance, not experimental AI policy
- Security, risk, compliance, and legal fully integrated
- Controls mapped to how AI is actually used today
- Infrastructure assessment to prevent rogue-AI incidents
- Built for Board, auditor, and regular scrutiny
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AI governance
Identify the AI Risk Owner, establish the Security Steering Committee, and define enterprise-level AI security principles.
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Acceptable use
Update your AUP to explicitly address AI: approved tools, data handling, IP protection, and enforcement alignment.
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AI training
Build staff capability on responsible AI use and threats: prompt injection, jailbreaks, and shadow AI.
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Vendor risk
Extend third-party risk assessments to AI vendors, integrated into the GRC platform with continuous monitoring.
Six Components. One Defensible Program.
Structure, Accountability & Security
A complete program built for the Board, the auditor, and the regulator. These efforts can also support a broader cybersecurity, IT audit, and compliance strategy across your organization.
AI Governance
Formalize AI Risk Owner, activate Security Steering Committee & define AI security principles with clear decision rights.
Acceptable Use
Update AUP: approved vs. prohibited AI use, data handling, IP protection & enforcement alignment.
Vendor Risk Management
Extend third-party risk assessments to AI vendors. Evaluate model training data, privacy controls & security posture in GRC.
AI Risk Assessment
NIST AI-RMF assessments across governance, data, model & operational domains—tracked in your GRC platform.
LLM Penetration Testing
Offensive: Adversarial testing aligned to MITRE ATLAS—prompt injection, data leakage, model abuse & evasion techniques. Configuration: Access controls, logging, model configuration & deployment architecture review.
Infrastructure & Controls Assessment
Assess whether AI agents operate within appropriate permission boundaries. Review least-privilege controls, confirmation guardrails, audit logging, backup architecure & environment segmentation to prevent autonomous destrucive actions.
AI Risk in the Real World
This Is Not an Industry-Specific Problem. It’s a Leadership Problem
HEALTHCARE EXAMPLE
Shadow AI exposes PHI: Staff use an unapproved LLM to summarize patient notes. No AI data-handling policy exists. PHI is retained in the vendor’s model. OCR inquiry follows.
FINANCIAL SERVICES EXAMPLE
Agent with Unconstrained Access: An automated workflow agent is granted broad database permissions. A logic error triggers mass deletion of transaction records. Regulators demand an incident report.
ANY ORGANIZATION EXAMPLE
Vendor AI Trains on Your Data: A productivity tool uses client inputs to improve its model. No contractual restriction exists. Client data becomes part of a commercial AI training dataset.
ENGAGEMENT MODEL
Four Phases. One Cohesive Engagement.
Typical 12–16 week engagement, customized to your pace and organizational readiness
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Discovery & Scoping
Catalog AI tools and vendors, and assess governance gaps and regulatory exposure.
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Design & Build
Develop the governance framework, update the AUP, configure the GRC platform, and build the training curriculum.
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Execute & Test
Deliver LLM pen testing, infrastructure review, and vendor risk assessments.
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Report & Sustain
Deliver the executive report, MITRE ATLAS mapping, remediation roadmap, and monitoring plan.
For organizations developing a broader AI strategy, our Data Analytics & AI services can provide support across AI readiness, strategy and implementation.
