Artificial intelligence is rapidly changing the way businesses operate, with AI agents increasingly being used to perform tasks that once required human employees. These systems can analyze information, interact with software, make decisions and complete actions with limited human supervision. But as organizations give AI agents more authority, a new challenge is emerging: what happens when an AI system behaves unexpectedly?

The growing risks associated with autonomous AI are forcing the cyber insurance industry to reconsider how it evaluates and covers technology-related threats. Insurers that traditionally focused on conventional cyberattacks such as ransomware, phishing and data breaches are now having to consider incidents caused or amplified by AI agents.

The shift could change the way companies purchase cyber insurance and how policies define responsibility when an automated system makes a costly mistake.

AI Agents Introduce a New Cyber Risk

AI agents differ from conventional software because they can operate with a degree of autonomy.

A traditional program generally follows predefined instructions. An AI agent, by contrast, can interpret information, select actions and interact with other systems based on its objectives.

Businesses are increasingly experimenting with agents for customer service, coding, financial analysis, administration and cybersecurity.

The benefits can be substantial. AI agents can work continuously, process large amounts of information and complete routine tasks quickly.

However, greater autonomy can also create new vulnerabilities.

An agent with access to company systems could potentially make an incorrect decision, expose confidential information or interact with an unsafe application.

In some circumstances, an AI agent could even be manipulated by an attacker.

This means insurers have to consider both traditional cyber threats and new risks created by autonomous technology.

Why Cyber Insurers Are Paying Attention

Cyber insurance exists to help organizations manage financial losses associated with cyber incidents.

Insurance companies assess risks before providing coverage and typically examine factors such as cybersecurity controls, data protection, access management and incident-response capabilities.

The arrival of AI agents complicates this assessment.

An organization might have strong cybersecurity protections but still experience an incident because an AI system was given excessive permissions or acted incorrectly.

Insurers therefore need to determine whether an AI-related incident should be treated as a cybersecurity event, a technology failure, an operational mistake or another type of loss.

The answer can influence whether a claim is covered.

The Problem of Autonomous Decision-Making

The biggest issue surrounding AI agents is autonomy.

Giving an AI system the ability to make decisions can increase productivity, but it also means that humans may not review every action before it occurs.

Consider an AI agent managing customer communications.

If it incorrectly shares confidential information, the company could face legal, regulatory and reputational consequences.

Similarly, an AI agent connected to financial systems could make an incorrect transaction or approve an inappropriate payment.

An agent used for software development could introduce a security vulnerability into a company’s systems.

These scenarios raise difficult questions about accountability.

Was the incident caused by the AI model, the company that deployed it, the employee who configured it or the software provider that created it?

Cyber insurers will increasingly need answers to such questions.

Traditional Policies May Not Be Enough

Existing cyber insurance policies were largely designed around recognizable risks.

Ransomware attacks, network intrusions, stolen credentials and data breaches are relatively familiar categories.

AI incidents can be much harder to classify.

An AI agent might be compromised by an external attacker, but the resulting damage could come from the agent’s own actions.

Alternatively, the agent might not be attacked at all.

It could simply misunderstand an instruction or generate an incorrect response that causes financial damage.

This creates potential gaps between traditional cyber coverage and emerging AI-related risks.

Insurers may respond by revising policy language or introducing specific conditions for companies using autonomous systems.

New Policy Conditions Could Emerge

Cyber insurers could require businesses to demonstrate stronger controls around AI agents.

These requirements might include permission limits, human oversight, monitoring systems and detailed records of agent activity.

Insurers could also ask organizations to identify which systems AI agents can access.

For example, an AI agent used for customer support may need access to customer information, but it may not require access to financial accounts or internal administrative systems.

Limiting permissions can reduce the potential damage caused by an unexpected action.

Businesses that demonstrate strong controls could potentially receive more favorable insurance terms.

Human Oversight Becomes More Important

Human oversight is likely to remain an important component of AI risk management.

Companies may need to determine which decisions can be made automatically and which require human approval.

Low-risk tasks could be handled entirely by AI.

High-risk actions involving payments, sensitive information or critical infrastructure could require a human confirmation.

This approach creates a balance between automation and control.

For insurers, evidence of meaningful human oversight could become an important part of evaluating an organization’s risk profile.

AI Agents Can Also Help Cybersecurity

The risk story is not entirely negative.

AI agents can also strengthen cybersecurity.

Security teams can use AI to monitor networks, identify suspicious behavior, investigate alerts and respond to potential threats.

An AI system can process huge quantities of security data much faster than a human team.

It may identify unusual patterns that would otherwise be overlooked.

This could allow businesses to respond to attacks more quickly.

However, using AI for cybersecurity creates another layer of risk.

If an autonomous security agent mistakenly blocks legitimate systems or deletes important information, the organization could suffer operational damage.

Insurers therefore need to consider both the protective benefits and the potential risks of AI-powered security systems.

Attackers Can Exploit AI Agents

One of the most significant concerns is that criminals could manipulate AI agents.

An attacker may attempt to influence an AI system through malicious instructions or compromised data.

If the agent has access to important business systems, manipulation could have serious consequences.

For example, an attacker could attempt to make an AI agent reveal sensitive information or perform unauthorized actions.

This creates a new category of cybersecurity concern.

Businesses will need to secure not only their traditional software and networks but also the instructions, permissions and connections surrounding AI agents.

The Importance of Access Controls

Access control will become increasingly important as companies deploy AI agents.

An agent should generally receive only the permissions required to perform its assigned task.

This principle, commonly associated with least-privilege security, can limit potential damage.

Companies can also separate AI systems from critical infrastructure.

If an agent used for marketing cannot access financial systems, an error or compromise within that agent is less likely to cause major financial damage.

Cyber insurers may increasingly examine these controls when assessing AI-related risks.

Monitoring AI Behavior

Continuous monitoring could become another requirement.

Companies need to know what their AI agents are doing.

Monitoring can help identify unusual activity, unexpected system access or repeated errors.

Organizations may also need logs showing decisions and actions taken by AI systems.

Such records can become valuable during an insurance claim because they may help establish what happened.

Without sufficient monitoring, it may be difficult for a company or insurer to determine whether an incident was caused by an attack, a software error or an AI decision.

Determining Responsibility

Liability is one of the hardest questions in the AI insurance market.

Multiple parties can be involved in an AI deployment.

There may be a model developer, an AI platform provider, a software vendor, a company deploying the agent and an employee responsible for configuring it.

If something goes wrong, determining responsibility can become complicated.

Insurance policies may need to clarify how responsibility is divided among these parties.

Businesses will also need to examine contracts with AI providers carefully.

Questions about indemnification, data handling and security responsibilities could become increasingly important.

AI Governance Could Influence Insurance Costs

Companies with mature AI governance programs may be better positioned to manage insurance risks.

AI governance can include policies covering how AI systems are selected, tested, monitored and retired.

Organizations may also create inventories of their AI systems.

Knowing where AI is being used can help businesses identify potential vulnerabilities.

For insurers, strong governance could become an indicator of lower risk.

Companies that cannot demonstrate adequate controls could potentially face higher premiums, lower coverage limits or additional exclusions.

The Role of Testing

Testing AI agents before deployment can also reduce risks.

Businesses can simulate different scenarios to determine how an agent responds to unexpected instructions.

Testing can reveal whether an agent might expose information, make unauthorized decisions or interact improperly with other systems.

Organizations can then modify the agent’s permissions or operating rules.

For insurers, evidence of systematic testing could become part of the underwriting process.

The more autonomous the AI system, the greater the need for testing may be.

Policy Exclusions May Change

Insurance policies frequently contain exclusions that define what types of losses are not covered.

AI could lead insurers to reconsider these exclusions.

Some policies may exclude losses caused by certain forms of automated decision-making.

Others could introduce specific AI-related conditions.

The industry may gradually develop clearer definitions for AI incidents.

This would help insurers price risks more accurately while giving businesses a better understanding of their coverage.

Clear policy language will be especially important as AI becomes more deeply integrated into business operations.

Businesses Face a New Risk Calculation

Companies adopting AI agents must consider more than technology costs.

They also need to evaluate operational, legal and insurance consequences.

An autonomous system may save money by reducing the need for manual work, but a single serious error could potentially create substantial losses.

Organizations therefore need to evaluate the overall risk before giving agents access to sensitive systems.

This does not mean companies should avoid AI.

Instead, businesses need to deploy the technology responsibly and establish clear limits.

Cyber Insurance Is Becoming More Dynamic

The emergence of AI agents demonstrates how quickly the cyber risk environment is changing.

Cyber insurers cannot rely entirely on historical data when evaluating technologies that are developing rapidly.

They may need to monitor emerging attack methods, AI capabilities and new forms of business risk.

Insurance products could become more customized.

Companies using highly autonomous AI may require different coverage from businesses using AI only for basic productivity tasks.

This could lead to a more sophisticated cyber insurance market.

Regulation Could Shape Insurance Policies

Governments and regulators are also examining AI risks.

As new AI regulations develop, insurers may use regulatory requirements as benchmarks when evaluating customers.

Companies that fail to comply with applicable AI or cybersecurity rules could face additional financial and legal exposure.

Regulation may therefore influence both the technology industry and the insurance sector.

Businesses will need to keep track of changing requirements as AI becomes more widespread.

Preparing for AI-Driven Incidents

Companies can take several practical steps to prepare.

They can create an inventory of AI agents and document what each system is allowed to do.

They can limit permissions, introduce human approval for high-risk decisions and continuously monitor agent activity.

Organizations should also establish clear incident-response procedures for AI-related failures.

If an AI agent behaves unexpectedly, employees need to know how to stop it quickly and investigate the cause.

Insurance coverage should also be reviewed regularly to determine whether emerging AI risks are adequately addressed.

The Future of AI and Cyber Insurance

The relationship between AI and cyber insurance is likely to become increasingly important.

As AI agents become more capable, companies may give them greater responsibility.

That could increase efficiency but also increase the consequences of mistakes.

Insurers will have to develop better ways of measuring these risks.

Over time, underwriting questionnaires may include detailed questions about AI use, autonomy, permissions and monitoring.

Premiums could also increasingly reflect the maturity of a company’s AI governance.

The insurance industry may eventually treat AI agents as a standard component of cyber risk rather than a separate emerging issue.

Conclusion

The rise of AI Agents is forcing cyber insurers to rethink how they understand technology risk.

Autonomous systems can provide major benefits by improving productivity, accelerating decision-making and supporting cybersecurity. But their ability to act with limited human intervention also creates new risks.

AI agents can make mistakes, expose information, interact with critical systems or potentially be manipulated by attackers. These possibilities make traditional cyber insurance models more complicated.

Insurers are likely to respond by examining AI governance, access controls, human oversight, testing and monitoring more closely. Policy language may also evolve to clarify which AI-related incidents are covered and which fall outside traditional cybersecurity protection.

For businesses, the message is clear: adopting AI responsibly will become just as important as adopting AI quickly.

Organizations that carefully control autonomous systems, limit permissions and maintain effective oversight may be better prepared for emerging risks.

As AI becomes more deeply embedded in everyday business operations, cyber insurance will have to evolve alongside it. The future of digital risk management will increasingly depend on the ability of businesses and insurers to understand not only what AI can do, but also what can happen when an AI agent goes rogue.

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