From Early Warning to Emerging Liability: AI's Next Phase in Public Entity Risk

Artificial intelligence (AI) is moving beyond simple office tools into systems that help public entities carry out their missions, supporting critical areas like:

• Emergency communications

• Public safety

• Transportation

• Construction

• Public services

However, despite its many use cases, AI can also disrupt operations and introduce new risks; for example, AI tools can result in inaccurate records, privacy events, cyber vulnerabilities and misplaced confidence.

The insurance question is where AI influences operations, what losses it may reduce or create and how responsibility is divided among the entity, its vendors, contractors, risk pool and insurers.

The Value of AI: From Retrospective Analytics to Operational Early Warning

For public entities, the best way to prevent losses is by identifying and mitigating risks before they can escalate into claims. AI allows organizations to analyze data, identify patterns and highlight conditions that require additional attention. For example, public entities often use a variety of tools to gather data about their operations, such as:

• Telematics

• Sensors

• Video

• Maintenance histories

• Incident narratives

• Weather information

• Workforce data

• Cyber activity

AI can help analyze and turn this large volume of data into actionable risk insights, helping public entities focus their attention on potential risks earlier.

Consider the following examples of how artificial intelligence led to improved loss control efforts:

• 9-1-1 Call Center: In November 2025, the National Telecommunications and Information Administration reported that AI-supported diversion reduced non-emergency call volume by as much as 40 percent at a 9-1-1 center in Jefferson County, Colorado, and approximately 36 percent on average in Monterey County, California.

• Law Enforcement Agency: A January 2026 National Institute of Justice study found that when an AI system was used to review selected Dallas Police Department body-worn-camera videos, the results did not differ significantly from those found by human reviewers. The results suggest AI could help agencies review large amounts of footage more efficiently and identify opportunities for training and improved performance.

AI has several use cases in preventing public entity claims, saving organizations from undue stress on tight budgets and lengthy legal processes. For example, AI can support:

• Fleet safety: AI can identify patterns such as repeated hard braking, prompting driver coaching, route review or vehicle inspection.

• Equipment and infrastructure: AI can detect changes in equipment or infrastructure data, helping organizations identify when earlier inspection or maintenance is needed.

• Law enforcement: AI can identify trends in complaints or use-of-force incidents that may warrant additional supervisory review, training, policy changes or equipment evaluation.

• Cybersecurity and fraud: AI can flag unusual payment or login activity, allowing organizations to pause transactions or independently verify activity before a potential loss occurs.

By enabling earlier intervention for high-risk areas, AI can improve safety and reduce claim frequency and severity over time, strengthening public entities' overall risk management programs.

New Technologies Introduce Opportunities and Risks

New technologies are being implemented directly where incidents can occur, presenting both opportunities to strengthen risk management as well as potential threats. For example, AI is often used at airports to support:

• Identity verification

• Passenger flow

• Ground operations

• Wildlife detection

• Inspection

• Maintenance

However, with new technologies like AI-powered facial recognition and biometric tools, new risks can emerge without proper oversight, such as privacy concerns, cybersecurity vulnerabilities and potential liabilities. For example, consider the growth in the use of generative AI (GAI) tools. GAI is becoming part of everyday tasks for public entities, helping to draft public communications, review documents or streamline employee workflows. However, AI can sometimes produce incorrect or made-up information, which, without going through verification, could end up in an official report or public notice, creating legal and reputational risks down the line.

As public entities like airports adopt connected systems, it is important to consider the following:

• Technology should support human judgment: Tools such as facial recognition can help identify potential issues, but human review remains important when making consequential decisions.

• Organizations must clearly define oversight roles for AI tools: Public entities should establish who owns, operates and oversees technology because unclear responsibilities can create liability and cybersecurity concerns.

• Technology can improve safety while creating new exposures: Drones, sensors, telematics and other tools can make inspections and infrastructure projects safer and more efficient, but public entities should consider how their use affects liability, workers' compensation, property and other insurance risks.

• Cybersecurity controls must be prioritized: AI is a double-edged tool for fraud prevention: it can help public entities detect suspicious activity, but it also gives criminals more sophisticated ways to impersonate trusted individuals. Public entities should strengthen verification and cybersecurity controls as AI-driven fraud becomes more convincing and difficult to detect.

The Importance of AI Governance

As AI use becomes more common, it is critical that public entities establish clear governance around how these tools are leveraged. Strong AI governance can help public entities determine when AI-driven insights are reliable and appropriate to use. Specifically, organizations should:

• Establish clear ownership and oversight of AI tools

• Evaluate the quality of the data and incorporate human review

• Document AI use and decisions

• Monitor AI performance and emerging risks

• Review insurance implications

These practices can help public entities capture the benefits of AI while reducing the potential for inaccurate decisions, data misuse and emerging liability.

The Future of AI and its Implications for Public Entities

Over the next several years, public entities are likely to incorporate AI in more areas of their operations to streamline everyday workflows and enhance public services. However, AI is expected to create new challenges as it continues to evolve, introducing new cybersecurity risks that can disrupt operations and prevent organizations from meeting their missions.

AI may also make claims and litigation more complex. Public entities could face questions about the information an AI system used, the recommendations it produced, whether employees reviewed those recommendations and whether the organization appropriately acted, or failed to act, on an AI-generated warning.

AI will additionally have a direct impact on insurance considerations. Insurers are likely to pay closer attention to how public entities use and manage AI. Applications may include more questions about AI tools, vendors, biometrics, employment systems and incident response, while policies may evolve to include new AI-related terms, limits or exclusions. Strong AI governance can demonstrate good risk management, but it does not necessarily mean lower premiums or broader insurance coverage.

How AI is Changing Insurance Risks and Claims

AI changes the insurance conversation for public entities both before and after a loss occurs.

• Before an incident: Tools such as telematics and sensors may help an entity identify warning signs like unsafe driving, deteriorating equipment, suspicious transactions or recurring operational patterns while there is still time to intervene. For underwriters, the more meaningful question is not whether an organization has purchased an AI tool, but whether it can explain how the system is used, who acts on its warnings, how performance is validated and whether the intervention has produced measurable safety or claims results.

• After an incident: AI-generated information can become part of the claim. For example, an AI-assisted police report, construction safety alert or maintenance warning could help determine what happened and how the organization responded. Claims professionals and counsel may need to establish which system and model version produced the output, what source data it used, whether the record was edited and who reviewed it. A public entity could face questions about whether it ignored an important warning or relied on an inaccurate one, potentially introducing legal liability.

AI-related incidents may also affect several types of insurance at once. For example, a compromised biometric system at an airport could create cyber, privacy, liability and business interruption concerns. On a major construction project, a missed safety alert or defective automated record could become relevant to workers' compensation, general liability, builder's risk, professional liability, delay or defect disputes. The insurance response will ultimately depend on the circumstances and the specific policy language.

Public entities should also understand how responsibility is divided among the vendors and other organizations involved in their AI systems. Agreements should establish who owns and may use the data, whether it may be used for model training, who preserves audit records, who investigates and corrects errors, how subcontractor failures are handled and how insurance, indemnification and liability limitations apply. Clear allocation cannot eliminate every dispute, but it can reduce uncertainty when an AI-related incident becomes a claim.

Alliant Public Entity: Preparing Organizations for the Next Phase of AI Risk

As AI becomes embedded in the everyday systems public entities rely on, organizations will need to manage both its loss-prevention opportunities and emerging risks. Alliant Public Entity can help organizations evaluate their evolving exposures, strengthen risk management strategies and develop comprehensive insurance programs as AI continues to reshape public-sector risk. For more information, reach out to an Alliant Public Entity specialist today.