AI GOVERNANCE, SECURITY & RISK MANAGEMENT
Adopt AI With Confidence.
Govern the Risks That Come With It.
Artificial intelligence is transforming how organizations operate, make decisions, and deliver value. But rapid AI adoption also introduces new cybersecurity, privacy, operational, and governance risks that traditional security programs may not fully address.
AI governance and cybersecurity risk assessments help organizations understand how artificial intelligence is being developed, deployed, and used across the business. These assessments evaluate AI-related security exposures, sensitive data handling, model and application risks, third-party dependencies, governance practices, and the effectiveness of existing safeguards.
Drawing on established frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001, assessments provide a structured, business-aligned approach to identifying material AI risks, strengthening accountability, and prioritizing practical improvements.
The objective is not to slow innovation. It is to establish the visibility, governance, and security foundations needed to adopt AI responsibly, protect business interests, and support sustainable growth.
THE BUSINESS CASE
63%
Of breached organizations studied either lacked an AI governance policy or were still developing one.
IBM Cost of a Data Breach 2025
97%
Of organizations reporting breaches of AI models or applications said they lacked appropriate AI access controls.
IBM Cost of a Data Breach 2025
$6M
Average cost of malicious breaches involving AI-enabled attacks in IBM's 2026 research, approximately $1 million above the global breach average.
IBM Cost of a Data Breach 2026
Artificial intelligence is becoming embedded in business operations, customer interactions, software development, and decision-making. Yet many organizations are adopting AI technologies without a complete understanding of the cybersecurity, privacy, governance, and operational risks they introduce.
AI-related exposure can emerge through unauthorized use of generative AI tools, sensitive information shared with external platforms, insecure model integrations, insufficient access controls, and reliance on third-party AI providers. These risks extend beyond traditional cybersecurity concerns to include model reliability, accountability, regulatory obligations, and the integrity of AI-supported business decisions.
An AI governance and cybersecurity risk assessment provides a structured evaluation of how artificial intelligence is used, managed, and secured across the organization. It identifies gaps in oversight, policies, technical safeguards, risk management practices, and third-party dependencies while establishing priorities aligned with business objectives.
The goal is not to restrict innovation or create unnecessary governance complexity. It is to provide the visibility, accountability, and security foundations needed to pursue AI opportunities responsibly, protect sensitive information, and maintain confidence in AI-enabled business operations.
WHAT I BRING
Connecting AI Innovation With Security, Governance, and Business Accountability
Artificial intelligence introduces risks that extend beyond traditional cybersecurity controls. Effective AI governance requires an understanding of how AI systems are developed, deployed, integrated, and used in business operations, along with the security, privacy, operational, and accountability considerations they introduce.
Experience spanning executive technology leadership, enterprise cybersecurity programs, and national cybersecurity advisory practice leadership provides a foundation for evaluating AI risk from both technical and business perspectives. This approach connects emerging AI security challenges with established principles of governance, risk management, data protection, and executive oversight.
Assessments incorporate recognized frameworks such as the NIST AI Risk Management Framework and ISO/IEC 42001 while considering AI-specific threats, including prompt injection, sensitive information exposure, insecure integrations, model misuse, and third-party AI dependencies. The emphasis is on identifying material risks and translating framework expectations into practical safeguards appropriate to the organization's size, operating environment, and AI adoption objectives.
The result is a structured, business-aligned approach to AI risk management that strengthens accountability, improves visibility into AI-related exposure, and supports informed decisions about the responsible development and use of artificial intelligence.
AI Governance Grounded in Business Reality
Evaluate AI use cases, decision-making responsibilities, acceptable use practices, and organizational oversight in the context of business objectives. Establish governance measures that address meaningful risks without unnecessarily restricting innovation
Security and Risk Management Across the AI Lifecycle
Assess data protection, model and application security, access controls, third-party dependencies, and AI-specific threats across development, deployment, and ongoing operations. Connect emerging AI risks with established cybersecurity practices and enterprise risk management.
Practical Framework Alignment and Improvement Roadmaps
Apply NIST AI RMF and ISO/IEC 42001 concepts to identify governance and security gaps, prioritize improvements, and define accountable actions. Translate assessment findings into a realistic roadmap that supports responsible AI adoption and continuous improvement.
WHAT I HELP WITH
Artificial intelligence introduces new opportunities and risks across business operations, technology environments, and decision-making processes.
Effective AI risk management begins with understanding how AI is used, what information and systems it interacts with, and whether appropriate safeguards and oversight are in place.
These capabilities help organizations identify AI-related exposure, evaluate governance and cybersecurity controls, strengthen accountability, and establish practical priorities for managing risk throughout the AI lifecycle.
AI Inventory & Use Case Risk Assessments
Identify and classify AI applications, models, platforms, and business use cases across the organization. Evaluate their purpose, data access, decision-making impact, business criticality, and potential exposure to establish visibility and risk-based oversight.
AI Governance, Policies & Accountability
Assess AI governance structures, acceptable use policies, roles, approval processes, and management oversight. Identify gaps in ownership, human oversight, risk acceptance, and organizational accountability for AI development, procurement, and deployment.
AI Model, Application & Cybersecurity Risk Assessments
Evaluate AI-specific threats such as prompt injection, insecure model integrations, excessive agent permissions, model misuse, and unauthorized access. Review security architecture, identity controls, application safeguards, and monitoring practices to identify weaknesses requiring remediation.
Third-Party AI & Supply Chain Risk Assessments
Assess how sensitive information is collected, processed, retained, and shared through AI systems. Evaluate data classification, access restrictions, privacy safeguards, training and retrieval data controls, and risks involving confidential information exposure or unreliable AI-generated outputs.
Enterprise Cybersecurity Risk Assessments
Evaluate cybersecurity and governance risks associated with external AI platforms, foundation model providers, embedded AI services, and technology vendors. Examine contractual safeguards, data handling, model dependencies, supplier assurance, and ongoing oversight of critical AI relationships.
NIST AI RMF & ISO/IEC 42001 Readiness Assessments
Evaluate AI governance and risk management practices against the NIST AI Risk Management Framework and ISO/IEC 42001. Identify gaps in policies, controls, lifecycle management, monitoring, and documented responsibilities, then develop a prioritized improvement roadmap aligned with organizational objectives.
FREQUENTLY ASKED QUESTIONS
As artificial intelligence becomes more deeply integrated into business operations, organizations face important questions about governance, cybersecurity, data protection, and accountability. Understanding how AI risks differ from traditional technology risks—and how established frameworks can help manage them—is essential to responsible adoption. The following questions address common considerations in evaluating AI governance and cybersecurity readiness.
What is an AI governance and cybersecurity risk assessment?
A cybersecurity risk assessment identifies and evaluates threats, vulnerabilities, and potential business impacts to determine where meaningful exposure exists. A cybersecurity maturity assessment evaluates how consistently and effectively an organization's security processes, controls, and governance practices are established and managed. Together, they provide a more complete understanding of both current cybersecurity capabilities and the risks those capabilities are intended to address.
How do AI cybersecurity risks differ from traditional cybersecurity risks?
AI systems face many traditional cybersecurity threats, including unauthorized access, data exposure, and insecure integrations. They also introduce additional risks such as prompt injection, model manipulation, sensitive information disclosure through generated outputs, unreliable responses, and excessive permissions granted to AI agents. Effective AI security combines established cybersecurity controls with safeguards designed for the unique behavior, data flows, and operational dependencies of AI systems.
What is the NIST AI Risk Management Framework?
The NIST AI Risk Management Framework (AI RMF) is a voluntary framework designed to help organizations identify, evaluate, and manage risks associated with artificial intelligence. It organizes AI risk management into four functions: Govern, Map, Measure, and Manage. The framework provides a structured approach to accountability, AI system understanding, risk evaluation, and ongoing risk treatment while allowing organizations to adapt practices to their business objectives and operating environments.
What is ISO/IEC 42001, and how does it relate to AI governance?
ISO/IEC 42001 is an international standard establishing requirements for an Artificial Intelligence Management System (AIMS). It provides a management system approach to AI policies, organizational responsibilities, risk assessment, lifecycle management, monitoring, and continual improvement. Organizations can use ISO/IEC 42001 to establish formal AI governance practices and pursue independent certification. It complements the NIST AI RMF by providing auditable management system requirements for governing AI-related activities.
Does an organization need an AI risk assessment if it only uses third-party AI tools?
Yes. Organizations using third-party AI platforms can still face significant risks involving confidential information, personal data, access permissions, contractual obligations, inaccurate outputs, and dependence on external providers. Even when an organization does not develop its own AI models, it remains responsible for determining how AI tools are approved, configured, used, and monitored. A risk-based assessment helps establish appropriate oversight and safeguards for externally provided AI capabilities.
What deliverables should an AI governance and cybersecurity risk assessment provide?
Typical deliverables include an inventory of identified AI systems and use cases, an evaluation of governance and cybersecurity practices, documented risk findings, and prioritized recommendations. Depending on the engagement, deliverables may also include AI policy recommendations, data protection and third-party risk findings, NIST AI RMF or ISO/IEC 42001 gap analysis, executive reporting, and an implementation roadmap. The goal is to establish clear accountability and practical priorities for managing AI-related risks.
TAKE THE NEXT STEP
Move Forward With AI. Without Losing Control of the Risks.
Artificial intelligence presents significant opportunities for innovation, efficiency, and growth. Realizing those benefits requires a clear understanding of how AI is being used, the risks it introduces, and whether appropriate governance and security safeguards are in place.
An AI governance and cybersecurity risk assessment helps identify material exposures, evaluate existing controls, and establish practical priorities for responsible AI adoption.
Build the visibility, accountability, and security foundations needed to pursue AI opportunities with confidence while protecting sensitive information, business operations, and organizational trust.