AI Risk Assessment

Many businesses have adopted AI more quickly than they have developed policies or oversight mechanisms to manage it. In many cases, employees begin using generative AI tools independently, while business applications quietly introduce AI functionality through software updates and new features. As a result, leadership teams often lack visibility into how AI is being used across the company.

Without a structured assessment process, businesses may inadvertently expose sensitive information, rely on inaccurate AI-generated outputs, create compliance challenges, or introduce risks that affect customers, employees, and business partners. These concerns become increasingly important as regulators, insurers, customers, and auditors place greater emphasis on responsible AI practices.

An AI Risk Assessment helps businesses understand their current level of exposure, identify gaps in governance, and develop practical controls that support secure and responsible AI use.

Tanner Security uses a methodology designed to evaluate both the technical and business risks associated with artificial intelligence. The assessment begins with an inventory of AI technologies currently in use throughout the business. This includes publicly available generative AI platforms, AI-enabled business applications, internally developed solutions, machine learning systems, and third-party AI services.

After identifying AI use cases, we assess how those technologies affect business processes, sensitive data, and regulatory obligations. We focus on cybersecurity risks such as unauthorized data exposure, prompt injection, insecure integrations, and other emerging threats to AI systems. We also review privacy requirements, intellectual property issues, model reliability, human oversight, and the risks posed by inaccurate or biased outputs.

The result is a AI-related risk assessment with actionable recommendations designed to improve governance, strengthen security controls, and support responsible adoption.

Technology alone cannot manage AI risk. Businesses need governance processes that establish accountability, define appropriate use, and create oversight that can evolve alongside AI technologies.

Tanner Security helps businesses build governance programs that support operational goals while addressing emerging AI risks. These programs often include AI policies, clearly defined roles and responsibilities, vendor review procedures, approved use case documentation, and processes for monitoring AI performance and compliance.

An effective governance program gives leadership visibility into AI use and helps to make sure that decisions involving artificial intelligence remain aligned with business goals, regulatory expectations, and customer requirements.

Our methodology aligns with the National Institute of Standards and Technology (NIST) AI Risk Management Framework (AI RMF), a widely recognized framework for managing AI risk.

The framework encourages businesses to establish governance structures, identify AI use cases, assess potential impacts, and implement controls that reduce risk over time. Aligning assessments with the NIST AI RMF gives businesses a structured way to evaluate AI systems while demonstrating a commitment to trustworthy, responsible AI practices.

This alignment can be especially valuable for companies in regulated industries, government contracting environments, or sectors where customers increasingly expect evidence of responsible AI governance.

Any business that uses artificial intelligence can benefit from an independent assessment. This includes companies using generative AI tools such as ChatGPT and Microsoft Copilot, businesses integrating AI into customer-facing applications, and firms developing AI-powered products or services.

AI risk assessments are especially valuable for healthcare providers, financial institutions, technology companies, manufacturers, professional service firms, and government contractors, where regulatory, contractual, or operational concerns may increase risk exposure.

As AI adoption expands, businesses that establish governance and oversight early are better positioned to scale AI initiatives confidently and responsibly.

Tanner’s AI Risk Assessment services provide a structured evaluation of your company’s AI ecosystem, governance maturity, and risk exposure.
We begin with an AI inventory and exposure review. Our assessment includes identifying where AI is currently in use, whether through internally developed systems, third-party platforms, embedded AI features, or generative AI tools used by staff.

We then assess risk across key domains, including data privacy and confidentiality, model reliability and accuracy, bias and fairness concerns, intellectual property implications, regulatory alignment, third-party vendor risk, and cybersecurity exposure.

Our assessment evaluates AI systems across their lifecycle, from design and acquisition through deployment, ongoing use, and monitoring. We examine whether appropriate controls exist for human oversight, output validation, documentation, escalation procedures, and continuous evaluation.
The result is a clear, defensible understanding of your AI risk posture, prioritized by impact and likelihood.

Our methodology aligns with the NIST AI Risk Management Framework (AI RMF) and emerging industry best practices. The NIST AI RMF provides a voluntary but increasingly influential structure for managing AI-related risks in a disciplined and defensible manner.

We help businesses operationalize AI governance across four core functions:

• Govern – Establishing policies, accountability structures, oversight roles, and executive-level ownership of AI use.
• Map – Identifying AI systems, use cases, data dependencies, and associated risk factors.
• Measure – Evaluating risk through testing, validation, monitoring, and documentation.
• Manage – Implementing safeguards, human oversight controls, incident response procedures, and continuous improvement processes.

For companies deploying generative AI tools, we incorporate considerations consistent with NIST’s Generative AI Profile, addressing risks such as hallucinations, content bias, output reliability, intellectual property concerns, and scale-related impact.

Our objective is not theoretical compliance. Practical governance withstands scrutiny from regulators, clients, and boards.

AI risk assessment and governance services are particularly valuable for:

• Professional services firms using AI in client deliverables
• Healthcare organizations handling sensitive patient data
• Financial institutions deploying AI analytics
• Defense contractors subject to regulatory scrutiny
• Organizations preparing for increased regulatory oversight
• Companies seeking to demonstrate responsible AI practices to clients

Businesses that proactively implement AI governance strengthen credibility, reduce liability exposure, and differentiate themselves in the marketplace.

Why Choose Tanner Security?

Tanner Security combines cybersecurity expertise, governance experience, and risk assessment capabilities to deliver practical guidance on AI-related risks. Our consultants bring extensive experience evaluating technology risks, security controls, compliance requirements, and governance programs across a wide range of industries.

Rather than relying on generic checklists or policy templates, we evaluate how AI is used in your environment and provide recommendations tailored to your business objectives, risk tolerance, and regulatory obligations.

Our assessments help leadership teams make informed decisions about AI adoption while maintaining confidence that risks are identified, managed, and monitored appropriately.

AI governance and AI risk assessments are closely related, but they serve different purposes.

An AI Risk Assessment is a point-in-time evaluation designed to identify and analyze the risks associated with artificial intelligence. The assessment helps businesses understand where AI is being used, what threats or concerns exist, and which controls may be needed to reduce exposure. Think of it as a diagnostic process that provides visibility into current risks and opportunities for improvement.

AI governance, on the other hand, is the ongoing framework used to manage AI throughout its lifecycle. Governance establishes the policies, procedures, oversight structures, accountability mechanisms, and monitoring activities that guide how AI is adopted and used over time.

A useful analogy is to compare AI governance to a cybersecurity program. A cybersecurity risk assessment identifies vulnerabilities and security gaps, while the cybersecurity program provides the policies, processes, and controls needed to manage those risks continuously. AI governance functions in much the same way.

Most businesses begin with an AI Risk Assessment to understand their current exposure. The assessment findings then serve as the foundation for building an effective AI governance program capable of supporting long-term, responsible AI adoption.