Managing AI Risk – A practical business guide to AI risk management, reliable outputs, privacy, vendor oversight, compliance, and incident response.
Who This AI Risk Management Guide Is For
This guide to AI risk management is intended for businesses that develop, purchase, configure, or allow employees to use AI-enabled systems. It explains how a risk-based AI governance program can address AI hallucinations, unreliable outputs, automation bias, human oversight, data privacy and security, vendor risk management, regulatory compliance, incident response, and documented accountability across business functions. The guidance is designed to help organizations manage artificial intelligence risks without prescribing a single control model or replacing the legal, technical, cybersecurity, privacy, compliance, or professional review required for a particular AI use case.
Why Managing AI Risk Matters | Basic AI Risk Management
Managing AI risk is an increasingly important business priority. Artificial intelligence can improve productivity, accelerate research, support customer service, analyze large volumes of information, and assist with business decision-making. At the same time, AI systems can produce inaccurate, incomplete, or misleading results. These risks are especially significant when employees rely on AI-generated content without appropriate review or when an organization uses AI in areas involving legal rights, financial consequences, safety, privacy, or regulatory compliance.
Three Core AI Risks for Organizations
Three related risks deserve particular attention: hallucinations, unreliable outputs, and automation bias. Hallucinations occur when an AI system generates information that appears plausible but is false, unsupported, or fabricated. Unreliable outputs include responses that are inaccurate, inconsistent, outdated, incomplete, or unsuitable for the particular business context. Automation bias occurs when people place excessive confidence in an automated recommendation and fail to question, verify, or override it.
Businesses cannot eliminate these risks entirely, but they can reduce their likelihood and potential impact through careful governance, technical controls, employee training, contract management, and human oversight. A risk-based program should be designed before AI tools are deployed widely and updated as the organization’s uses of AI evolve.
Executive Overview: Key AI Risk Management Controls
Businesses can reduce AI risk by identifying higher-risk uses, requiring source verification and meaningful human review, protecting confidential information, monitoring system performance, and assigning clear accountability. The controls described below should be calibrated to the potential consequences of an error rather than applied uniformly to every AI tool.
AI Hallucinations, Unreliable Outputs, and Automation Bias Explained
AI Hallucinations
An AI hallucination is an output that is presented with confidence but is not supported by reliable facts or source material. A system may invent a citation, misstate a contract provision, attribute a quotation to the wrong person, create a nonexistent case, or provide a fabricated statistic. Hallucinations can occur because generative AI systems are designed to predict and produce likely sequences of text rather than independently determine whether each statement is true.
Hallucinations are not limited to unusual or poorly worded requests. They may arise in otherwise ordinary business applications, particularly when the system lacks access to authoritative information, receives ambiguous instructions, is asked about specialized subjects, or is encouraged to provide an answer even when relevant information is unavailable.
The consequences can be substantial. A fabricated legal authority may undermine a filing or business decision. An invented technical specification may cause a product or safety problem. An incorrect medical, financial, or compliance-related statement may expose a company to regulatory scrutiny, customer claims, contractual disputes, or reputational harm.
Unreliable or Inconsistent Outputs
Even when an AI system does not hallucinate, its output may not be reliable enough for the intended use. The system may omit an important qualification, misunderstand the user’s request, apply the wrong jurisdiction, use outdated information, or produce different results for similar inputs. Small changes in wording can sometimes result in materially different answers.
Reliability problems may also arise from the data used to develop, configure, or operate an AI system. Poor-quality, incomplete, biased, or improperly labeled data can affect performance. A system may perform well in testing but produce weaker results when applied to a different population, product line, language, jurisdiction, or operational environment.
Businesses should distinguish between an AI output that is useful as a draft or research aid and an output that is sufficiently dependable to support an automated or high-impact decision. The appropriate level of review depends on the use case, the potential harm, the sensitivity of the information involved, and the organization’s legal and contractual obligations.
Automation Bias
Automation bias is the tendency to favor information produced by an automated system over independent judgment, even when the automated information is incomplete or wrong. It can affect experienced professionals as well as less experienced users. A polished answer, numerical score, or confident recommendation may appear objective and authoritative, causing users to overlook warning signs.
Automation bias is more likely when employees are under time pressure, lack sufficient subject-matter expertise, receive limited training, or are evaluated primarily on speed and volume. It can also develop gradually when an AI system performs well often enough that users stop checking its work.
Organizations should treat human review as a meaningful control, not as a formality. A person who simply approves an AI output without understanding it, checking the underlying information, or having authority to reject it may not provide effective oversight.
Legal, Regulatory, and Operational Risks of AI
AI Legal Liability and Regulatory Compliance Risks
AI-related errors can create exposure under multiple legal theories. Depending on the facts, a company may face allegations involving negligence, breach of contract, consumer protection, false advertising, discrimination, privacy violations, professional responsibility, intellectual property, product liability, or failure to comply with industry-specific requirements.
AI-Generated Business Records and Explainability
AI outputs may also become part of the company’s business records, customer communications, internal investigations, or submissions to a regulator or court. If an organization cannot explain how an output was generated, reviewed, and approved, it may have difficulty demonstrating reasonable controls or defending the accuracy of its decision-making process.
Contractual Risk and Third-Party AI Accountability
Contractual obligations deserve particular attention. A company may be responsible for the accuracy, confidentiality, security, or regulatory compliance of work performed by a vendor, even when the vendor used an AI system. Agreements should therefore address permitted uses of data, confidentiality, security, subcontractors, intellectual property, audit rights, incident notification, performance standards, and responsibility for errors.
AI Transparency and Accuracy in Stakeholder Communications
Organizations should also consider whether the use of AI is consistent with representations made to customers, employees, business partners, investors, or regulators. Statements about human review, accuracy, privacy, security, or the capabilities of an AI-enabled product should be accurate and supportable.
Article licensed from Attorney John Fisher
Building a Risk-Based AI Governance Program
Create an Enterprise AI Inventory
A practical AI governance program begins with an inventory of how the organization uses, develops, purchases, and embeds AI. The inventory should include approved tools, business owners, data inputs, outputs, users, vendors, affected individuals, and the decisions influenced by each system.
Apply Proportionate AI Risk Controls
Not every AI use requires the same controls. A tool used to suggest subject lines for internal email presents a different risk profile from a system used to screen applicants, evaluate creditworthiness, recommend medical treatment, approve insurance claims, monitor workplace conduct, or operate safety-sensitive equipment.
Classify AI Uses by Risk
Businesses should classify use cases according to potential harm and required oversight. Relevant factors may include:
- Whether the system affects a person’s legal rights, employment, access to services, finances, safety, or reputation;
- Whether the output is customer-facing, externally published, or submitted to a court, regulator, or other authority;
- Whether the system processes personal, confidential, proprietary, health, financial, or regulated information;
- Whether an error could cause physical injury, discrimination, material financial loss, or significant reputational damage;
- Whether the output can be independently verified before action is taken;
- Whether the system operates autonomously or merely assists a qualified professional; and
- Whether applicable law, regulation, contract, or professional standards impose additional requirements.
Higher-risk applications should generally require formal approval, documented testing, designated accountability, stronger access controls, ongoing monitoring, and meaningful human review. Some uses may warrant prohibition unless the organization can establish adequate safeguards.
Assign Clear Accountability
Every material AI use should have an identified business owner who is responsible for the system’s purpose, performance, data, controls, and continued appropriateness. Responsibility should not be assigned solely to an information technology department. Legal, compliance, privacy, security, human resources, procurement, risk management, and relevant business teams may all need to participate.
Governance documents should identify who may approve a use case, who may configure the system, who may rely on its output, who must review it, and who may suspend or discontinue its use. Employees should understand that using an AI tool does not transfer accountability for the resulting work.
AI Risk Controls for Hallucinations and Unreliable Outputs
Use Authoritative and Appropriate Data
Where possible, AI systems should be connected to current, authoritative, and relevant business information rather than being asked to answer from general model knowledge alone. Organizations should maintain clear source repositories and determine which materials are approved for use in particular workflows.
Source materials should be reviewed for accuracy, completeness, version control, and access permissions. A system connected to outdated policies, superseded contracts, or incomplete product information may produce an answer that is internally consistent but still wrong for the organization’s current circumstances.
Require Source-Based Responses
For research, compliance, legal, financial, technical, and other high-consequence uses, the system should be configured to identify the materials supporting its response. Users should be able to inspect those materials and confirm that they actually support the conclusion. A citation or link alone is not sufficient if the cited source is nonexistent, irrelevant, or inconsistent with the generated statement.
Employees should be instructed to verify primary sources rather than relying on AI-generated summaries. For example, a legal researcher should review the actual statute, regulation, case, or official guidance; a finance professional should inspect the underlying records; and a technical employee should confirm specifications against approved documentation.
Limit the System’s Scope
AI systems are generally more reliable when they are given a clearly defined task, relevant context, and explicit boundaries. Prompts and workflow instructions should specify the applicable jurisdiction, time period, audience, terminology, source materials, and output format when those details matter.
Systems should be instructed to identify uncertainty and decline to answer when the available information is insufficient. A refusal or escalation is often safer than a confident answer based on unsupported assumptions. Organizations should also limit systems from making claims beyond their tested capabilities.
Use Structured Workflows and Validation
Important outputs should pass through validation steps before they are used. Depending on the application, validation may include numerical checks, comparison with a source database, rule-based testing, duplicate review, exception reporting, or approval by a qualified professional.
For customer-facing communications, businesses may require review of factual claims, pricing, eligibility statements, legal terms, and representations about products or services. For code-generation tools, organizations should use code review, security scanning, testing, and deployment controls. For document-analysis tools, users should confirm that relevant sections were not omitted or misclassified.
Test Before and After Deployment
Pre-deployment testing should use representative examples, difficult edge cases, known failure scenarios, and inputs that reflect the actual operating environment. Testing should assess not only whether the system produces useful results but also how it fails, how often it fails, and whether users can detect those failures.
Post-deployment monitoring is equally important. Model behavior can change because of software updates, data changes, configuration changes, new users, shifting business conditions, or changes made by a third-party provider. Organizations should establish measurable performance thresholds and investigate unusual error rates, complaints, overrides, or inconsistent results.
Human Oversight Controls to Reduce Automation Bias
Make Human Review Meaningful
Human review should be tailored to the risk of the decision. Reviewers should have sufficient knowledge, time, access to relevant source materials, and authority to reject or modify the AI output. They should not be required to approve an answer merely to keep a workflow moving.
For significant decisions, the reviewer should document the basis for approval, identify material disagreements with the system, and record any additional information considered. The organization should also provide a clear escalation path for uncertain or contested results.
Present Uncertainty and Alternatives
User interfaces can either reduce or increase automation bias. Systems should avoid presenting uncertain recommendations as definitive conclusions. Where appropriate, they should display supporting sources, confidence limitations, relevant exceptions, alternative options, and prompts asking the reviewer to confirm key facts.
Organizations should be cautious with numerical scores or rankings that appear precise but lack a reliable factual basis. A score can create a stronger impression of objectivity than a narrative explanation, even when both are based on uncertain information.
Separate Recommendations From Decisions
In high-impact settings, AI should generally support rather than replace accountable decision-makers. The responsible person should independently consider the relevant facts and applicable criteria instead of adopting the recommendation automatically.
Policies should identify decisions that may not be made solely by an AI system. They should also address when an individual affected by an AI-assisted decision may request an explanation, correction, reconsideration, or human review, subject to applicable law.
Train Employees on Failure Modes
Training should cover more than how to operate a particular tool. Employees should learn that AI can fabricate information, omit important details, reflect problematic assumptions, expose confidential data, and produce different answers to similar questions. Training should include realistic examples from the organization’s own workflows.
Employees should be taught to ask: What facts support this output? What facts may be missing? Is the source current and authoritative? Could the answer be wrong in a way that matters? Who could be harmed if the output is incorrect? What additional review is required before action is taken?
AI Data Protection, Privacy, and Confidentiality
Control Sensitive Data Entered Into AI Systems
Businesses should determine what information may be entered into each AI tool. Employees should not assume that a system is confidential merely because it is accessed through a corporate account. Depending on the provider, prompts and uploaded materials may be retained, reviewed, used for service improvement, or made available to other authorized users.
AI Privacy, Security, and Access Management
Organizations should establish rules for personal information, trade secrets, privileged communications, customer records, employee information, health information, financial data, security credentials, and other sensitive material. Technical controls may include data-loss prevention, access restrictions, encryption, logging, retention limits, tenant separation, and blocking unapproved applications.
Protect Privileged and Confidential Information
Legal departments should consider whether use of an AI system could affect attorney-client privilege, work-product protection, confidentiality obligations, discovery preservation, or records-management requirements. Employees should know which systems are approved for confidential or privileged work and which are not.
AI Vendor Risk Management and Contract Considerations
Conduct AI Vendor Due Diligence
Before adopting an AI service, a business should evaluate the provider’s security practices, data practices, model limitations, subcontractors, service commitments, update procedures, and incident-response process. Marketing statements should not be treated as a substitute for contractual protections or technical diligence.
Key AI Contract Terms and Protections
Depending on the use case, contracts should address:
- How customer data and prompts may be collected, stored, accessed, retained, and deleted;
- Whether submitted information may be used to train or improve a model;
- Confidentiality and security obligations;
- Data location, subcontractors, and cross-border transfers;
- System availability, performance, accuracy, and support commitments;
- Notice and cooperation concerning security incidents or material system changes;
- Audit, testing, and information rights;
- Intellectual property ownership and rights to use inputs and outputs;
- Allocation of responsibility for infringement, privacy violations, and other claims; and
- Termination assistance, data return, deletion, and business continuity.
A company should also assess whether its own agreements permit the use of AI in performing services or handling customer information. Restrictions may appear in confidentiality clauses, data-processing terms, professional-services agreements, procurement documents, or industry-specific requirements.
AI Incident Response, Monitoring, and Documentation
Define and Identify AI Incidents
AI incidents should be incorporated into the organization’s existing incident-response framework. Examples include a fabricated customer communication, disclosure of confidential information, discriminatory output, incorrect regulatory submission, harmful recommendation, security vulnerability, or material error that reaches a customer or decision-maker.
Establish an AI Incident Response Process
Response procedures should identify who must be notified, how use of the system can be paused, how affected information and outputs will be preserved, how the scope of the incident will be assessed, and whether legal, contractual, regulatory, or customer notifications may be required.
Document AI Governance and Corrective Actions
Documentation is an important risk-control tool. Businesses should maintain records of approved use cases, risk assessments, testing, training, system changes, human approvals, incidents, complaints, and corrective actions. Documentation should be proportionate to the risk but sufficient to show that the organization considered foreseeable failures and implemented reasonable safeguards.
How to Implement an AI Risk Management Program
- Create an AI inventory. Identify all approved, unapproved, purchased, internally developed, and embedded AI systems.
- Classify each use case. Consider the information processed, people affected, decisions influenced, and potential consequences of error.
- Assign an accountable owner. Identify the person or team responsible for the system’s purpose, controls, performance, and continued use.
- Approve permitted uses. Establish clear rules for low-, medium-, and high-risk applications, including prohibited activities.
- Control inputs. Define which data may be submitted and apply appropriate privacy, confidentiality, security, and access restrictions.
- Use authoritative sources. Connect systems to current and approved information where appropriate, and require source verification for important outputs.
- Test failure modes. Evaluate hallucinations, omissions, inconsistency, bias, security weaknesses, and performance under realistic conditions.
- Require meaningful human review. Give reviewers the expertise, time, information, and authority needed to challenge the system.
- Monitor performance. Track errors, overrides, complaints, incidents, and changes in system behavior after deployment.
- Train users. Teach employees how to recognize unsupported outputs, automation bias, privacy risks, and escalation requirements.
- Review vendor terms. Address data use, confidentiality, security, intellectual property, system changes, incidents, and liability.
- Maintain records. Document approvals, testing, decisions, reviews, incidents, and corrective actions.
- Reassess regularly. Update the organization’s controls when the technology, business process, law, vendor, data, or risk profile changes.
Conclusion: A Practical Approach to Managing AI Risk
AI hallucinations, unreliable outputs, and automation bias are manageable risks, but they are not risks that can be addressed by instructing employees to “use common sense” or by adding a nominal human approval step. Effective risk management requires a coordinated program that combines appropriate use-case selection, reliable data, source verification, testing, technical safeguards, employee training, meaningful human judgment, vendor oversight, and documented accountability.
To manage AI risk effectively, businesses should begin with the uses of AI that could cause the greatest harm if the system is wrong. They should identify accountable owners, verify authoritative sources, test foreseeable failure modes, establish meaningful human review, protect sensitive information, monitor system performance, and document decisions and corrective actions. Controls should then be extended and calibrated across lower-risk uses as appropriate. A company that treats AI outputs as potentially useful but not inherently trustworthy will be better positioned to capture the benefits of artificial intelligence while reducing legal, operational, financial, privacy, compliance, and reputational exposure.
Legal Disclaimer
This article provides general information and is not legal advice. Businesses should consult qualified counsel regarding the laws, regulations, contracts, and specific facts applicable to their AI systems and operations.
