AI Risk: A Readiness Checklist for Legal, Privacy, and Cybersecurity Teams

July 2025

Overview

Artificial Intelligence (AI) is transforming how organizations operate, innovate, and compete. It enables automation, accelerates data-driven decision-making, and enhances customer experiences across functions—from operations and marketing to HR and legal. As AI becomes more deeply embedded in enterprise workflows, the risks associated with its use are drawing increased attention from regulators, stakeholders, and the public.

The regulatory environment is advancing rapidly. The EU AI Act introduces a risk-based framework that classifies AI systems by level of impact, imposing conformity assessments and transparency requirements on high-risk systems. In the U.S., agencies like the National Institute of Standards and Technology (NIST) have published guidelines, while state-level legislation is beginning to fill regulatory gaps. Industry-specific compliance obligations and global privacy laws such as the General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA) add further layers of complexity.

These developments signal a broader shift: organizations are now expected to actively manage AI-related risks, not just react to them. This includes understanding how AI is used internally and externally, identifying who owns and operates the systems, and aligning governance structures with the evolving legal and ethical landscape. Use cases involving personal data, automated decision-making, or third-party AI tools carry heightened risk and require tailored safeguards.

Meeting these obligations requires a coordinated, cross-functional approach and ongoing oversight. Legal, privacy, compliance, IT, and business leaders must work together to evaluate internal policies, data practices, procurement procedures, and product development pipelines. Risk mitigation must be built into each phase—from acquisition and design to deployment and monitoring. Vendor agreements, employee training, and documentation practices must also be revisited.

This checklist provides a framework for evaluating AI readiness. It covers nine essential areas, including inventorying AI use, assessing use-case risk, formalizing governance, classifying systems by risk level, managing data privacy and cyber risk, mitigating bias, and ensuring legal compliance. Each section includes practical questions and process considerations that teams can adapt to their business model, regulatory exposure, and maturity level.

AI readiness is not simply about regulatory compliance. Organizations with clear AI governance, auditability, and transparency protocols are better positioned to meet procurement standards, attract enterprise clients, and mitigate reputational risk. These capabilities are increasingly viewed as differentiators in client pitches, investor due diligence, and public-sector contracting.

As AI continues to scale across industries, responsible implementation will be a key factor in long-term success. By adopting a proactive readiness strategy, organizations can manage downside risk while enabling innovation that is secure, ethical, and aligned with stakeholder expectations.

AI Readiness Checklist

1. Identify AI Use Across the Organization

   Catalog all known and informal AI tools in use—including code generation, predictive analytics, monitoring, automated decision-making, and text/image generation—to create transparency and guide governance efforts.

   • Code-generating AI (R&D, IT)
   • Predictive AI (inventory management, logistics, maintenance, staffing)
   • Monitoring AI (security, analytics, operations)
   • Decision-making AI (hiring, sales and marketing, advertising, loans)
   • Text-generating AI (chatbots, customer service, document drafting, templates)
   • Image-generating AI (design, marketing, other content)
2. Assess Risk Based on Specific AI Use Cases

   Determine risk exposure by understanding how AI tools are used, whether off-the-shelf or proprietary, and whether the organization is a developer, deployer, or end user.

   • Using off-the-shelf AI solutions
   • Providing data for AI training
   • Using foundational models
   • Developing products or services using foundational models
   • Deploying AI in end-user-facing applications
   • Identifying the legal and regulatory frameworks that apply to each use case
3. AI Governance and Policy Development

   Establish centralized policies, oversight structures, and training programs to promote transparency, ethical use, and legal alignment across the organization.

   • Establish a company-wide AI policy
   • Form an AI oversight committee
   • Set policies for data handling, access, storage, and retention
   • Ensure alignment with legal and ethical standards
   • Document decisions and conduct regular risk assessments
   • Train employees on responsible use and compliance
4. Classify Risk Levels (Aligned with EU AI Act)

   Apply risk classification frameworks—such as those from the EU AI Act—to assess whether AI systems fall into unacceptable, high-risk, limited-risk, or minimal-risk categories and what obligations apply.

   • Identify unacceptable, high-risk, limited-risk, and minimal-risk applications
   • Understand roles and responsibilities (e.g., provider, deployer)
   • Determine need for conformity assessments or transparency measures
   • Maintain human oversight and fairness across all AI use cases
5. Product Development and Risk Mitigation

   Incorporate legal and ethical review into the design of AI-enabled products or tools. Ensure documentation, disclosures, and limitations are clear and defensible.

   • Ensure systems perform as intended
   • Prevent bias amplification
   • Track compliance with AI regulations
   • Include required disclosures and limitations
   • Assess liability and manage it contractually and technically
6. Data Privacy Compliance

   Confirm appropriate data rights, consents, and usage terms are in place. Ensure alignment with global privacy laws and internal privacy policies.

   • Confirm data usage rights
   • Obtain necessary consents
   • Ensure data accuracy and reliability
   • Clarify post-termination data rights
   • Impose contractual obligations for data sharing
   • Ensure privacy law compliance (e.g., GDPR, CCPA, HIPAA)
   • Update privacy policies and public disclosures
7. AI Bias and Ethical Considerations

   Regularly test for bias and ensure outputs are explainable and equitable. Avoid black-box systems when possible, and demand transparency from third-party vendors.

   • Test models for bias regularly
   • Implement mitigation steps
   • Establish internal explainability policies
   • Document supplier decision-making processes
8. AI Security and Cyber Risk Management

   Identify potential vulnerabilities introduced by AI, and integrate AI-specific threats into cybersecurity planning, incident response, and third-party audits.

   • Identify new vulnerabilities introduced by AI
   • Use technical and contractual controls
   • Develop incident response protocols
   • Audit upstream AI suppliers
9. Legal and Regulatory Compliance

   Monitor evolving laws and standards, review vendor and licensing contracts, and embed compliance controls into procurement and development processes.

   • Track evolving frameworks (EU AI Act, NIST AI RMF)
   • Review contracts for IP, privacy, and confidentiality risks
   • Align use with contractual/licensing terms
   • Exercise caution with free/open-source AI tools and changing terms

Summary and Action Items

AI offers extraordinary potential to transform operations, generate new revenue streams, and strengthen customer relationships. However, these gains will only materialize for organizations that manage risk with energy equal to that used to pursue innovation.

This checklist will equip business, legal, and IT leaders to evaluate current AI exposure and prioritize improvements. AI governance should be viewed not only as a regulatory requirement but as a business enabler. The organizations that thrive will be those that implement safeguards early, adapt to evolving standards, and clearly demonstrate responsible use.

This checklist is intended as both an educational tool and a roadmap for action. As a next step, organizations should:

• Develop an AI policy and identify an AI governance/oversight team
• Conduct an AI audit across departments and tools
• Update governance frameworks to reflect evolving laws and risks
• Prioritize review of high-risk use cases
• Embed privacy and cybersecurity controls into design workflows
• Monitor developments in AI law and adapt policies accordingly

By taking a proactive approach to AI risk, organizations can reduce exposure, enable responsible innovation, and position themselves as credible, trustworthy adopters of AI.