Conducting an AI Risk Assessment

Summary

Bloomberg Law's practical guide cuts through the theoretical noise to deliver a concrete methodology for AI risk assessment that actually works in corporate environments. Rather than offering another abstract framework, this resource provides step-by-step processes for identifying potential AI harms, calculating their likelihood, and building documentation that satisfies both legal teams and regulatory requirements. The guide bridges the gap between technical risk analysis and business governance, offering templates and workflows that can be immediately implemented across different AI use cases.

What makes this different

Unlike academic risk frameworks that focus on theoretical categorization, this Bloomberg Law guide is built for practitioners who need to deliver risk assessments under time pressure and regulatory scrutiny. The methodology emphasizes practical harm identification over comprehensive taxonomies, focusing on risks that actually matter to business operations and legal compliance. The documentation templates are designed to withstand audit review while remaining accessible to non-technical stakeholders who make governance decisions.

The guide distinguishes between "assessment theater" and genuine risk evaluation, providing criteria for determining when an AI system requires deep analysis versus standardized review processes. This tiered approach acknowledges that not every AI implementation needs the same level of scrutiny while ensuring high-risk applications receive appropriate attention.

Core methodology breakdown

Harm identification phase: The guide provides structured questioning techniques for uncovering potential AI-related harms, including direct system failures, indirect consequences of accurate outputs, and systemic impacts over time. Rather than starting with pre-defined risk categories, the methodology helps teams identify context-specific harms based on their actual AI applications.

Probability assessment: Bloomberg Law's approach combines quantitative metrics with qualitative judgment calls, recognizing that many AI risks can't be precisely calculated but still need systematic evaluation. The guide offers calibration techniques for improving probability estimates and methods for handling uncertainty in risk calculations.

Mitigation strategy development: Each identified risk receives specific mitigation recommendations, with clear guidance on choosing between prevention, detection, and response strategies. The methodology includes cost-benefit analysis frameworks for comparing different mitigation approaches and determining acceptable residual risk levels.

Documentation standards: The guide provides templates that translate technical risk assessments into language that legal, compliance, and executive teams can understand and act upon. Documentation formats are designed to demonstrate due diligence while avoiding unnecessary complexity.

Who this resource is for

Legal and compliance teams implementing AI governance programs need practical risk assessment methods that produce defensible documentation. This guide provides the structured approach and templates necessary for building repeatable risk evaluation processes.

Risk management professionals tasked with extending traditional risk frameworks to AI applications will find concrete methodologies that integrate with existing enterprise risk management systems while addressing AI-specific challenges.

Product and engineering leaders responsible for AI system deployment can use this guide to establish risk assessment workflows that identify genuine concerns without creating bureaucratic bottlenecks that slow development cycles.

Internal audit and governance teams need standardized approaches for evaluating AI risk assessments across different business units and applications. The guide's documentation standards and process requirements support consistent evaluation criteria.

Implementation roadmap

Start with pilot assessments on 2-3 representative AI systems to calibrate the methodology for your organization's risk tolerance and documentation requirements. The guide includes selection criteria for choosing appropriate pilot systems and metrics for evaluating methodology effectiveness.

Develop internal expertise through structured training on the harm identification and probability assessment techniques. Bloomberg Law provides specific exercises for improving risk evaluation skills and avoiding common assessment biases.

Build documentation workflows that integrate with existing compliance and audit systems while meeting the guide's standards for risk assessment records. This includes establishing review cycles and approval processes for different types of AI implementations.

Scale the methodology across different AI use cases by developing system-specific templates while maintaining consistency in core evaluation principles. The guide offers adaptation strategies for different types of AI applications and risk contexts.

Watch out for

The methodology assumes access to technical information about AI systems that may not always be available, particularly for third-party AI services. Organizations need fallback assessment approaches for evaluating risks in AI systems with limited transparency.

Risk assessment quality depends heavily on the expertise and judgment of the assessment team. The guide provides some calibration techniques, but organizations should plan for ongoing training and external validation of their risk evaluation capabilities.

Documentation requirements can become compliance theater if not properly implemented. Focus on creating risk assessments that actually inform decision-making rather than simply satisfying audit requirements.