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Freedom251 › Blog › AI security

How AI Is Reshaping Cyber Risk—and What Insurers Now Assess

By Freedom251 Team · · 11 min read

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Yes. AI is changing cyber risk by making attacks faster, cheaper and more convincing while giving defenders better detection, triage and response tools. The insurance problem is no longer limited to whether a company has a firewall: underwriters must evaluate AI use cases, model and data dependencies, human approvals, third parties and the possibility that one shared provider can cause losses for many policyholders. Cyber insurance can transfer part of the resulting financial loss, but it does not prevent an intrusion or replace identity controls, patching, backups, vendor oversight and rehearsed response.

What has AI changed about cyber risk?

AI changes the economics and tempo of familiar threats rather than creating one single new category of loss. An attacker can automate research, generate tailored messages and imitate a trusted person at a scale that would previously require a large criminal team. A defender can use similar capabilities to find anomalies, prioritize alerts and contain incidents. The net effect depends on how an organization deploys the technology, who controls the underlying data and how quickly people verify machine-generated decisions.

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Attackers gain speed, scale and realism

  • Phishing and social engineering: Language models can produce fluent, personalized messages in many languages and adapt a campaign after a target replies. That raises the chance that an employee will approve a fraudulent payment or disclose credentials.
  • Impersonation and deepfakes: Generated voice, video or images can make a request appear to come from an executive, supplier or customer. A convincing fake is especially dangerous when payment approval relies on a single communication channel.
  • Vulnerability discovery: Automated tools can search code, configurations and public information for weaknesses more quickly. The New York State Department of Financial Services said on 16 October 2024 that AI has created opportunities for cybercriminals to commit crimes “at greater scale and speed.”
  • Document manipulation: AI can alter invoices, identity documents and other records, complicating fraud detection and claims investigations.

The National Association of Insurance Commissioners (NAIC) likewise reports that AI can help attackers find vulnerabilities, create convincing phishing messages, manipulate documents and produce deepfakes more quickly. These are capability changes; they are not proof that every incident was caused by generative AI.

Defenders also gain leverage

Security teams can apply machine learning to endpoint, identity, email and network telemetry, summarize large alert queues and identify unusual behavior sooner. AI-assisted analysis can reduce the time between detection and containment, but it can also produce false positives, miss a novel attack or expose sensitive data to an external model. Human review, logging and a tested fallback process remain necessary.

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How should a company assess AI-related cyber risk?

Use a scenario-and-portfolio assessment. Start with each concrete AI use case, then trace the assets, data, providers, access paths, people and recovery dependencies that make a loss possible. Assess both the additional attack opportunity and the defensive benefit instead of treating “AI” as a single risk score.

  1. Inventory uses and boundaries. Record internal models, employee use of public tools, embedded AI in software, autonomous workflows and vendor-operated systems. Identify whether each system can read, write, send messages, approve transactions or call other services.
  2. Map critical data and decisions. Classify personal, financial, regulated and proprietary data. Document which model receives it, where it is stored, how long it is retained and whether outputs influence payments, access, safety or customer communications.
  3. Trace providers and access paths. Name the cloud, model, software and managed-service suppliers behind each use case. Record authentication methods, privileged accounts, application programming interfaces, network connections and subcontractors.
  4. Mark human approval points. Specify when a person must validate an output, a payment instruction, a code change or a security action. Test whether that reviewer has enough context and time to detect a plausible false answer.
  5. Test attack enablement. Run realistic exercises for credential phishing, executive or supplier deepfakes, prompt or data exfiltration, manipulated documents, model compromise and automated exploitation of an unpatched weakness.
  6. Measure defensive value. Compare alert quality, detection time, containment time and analyst workload with and without the AI control. Keep evidence of false-positive rates and the manual process used when the model is unavailable.
  7. Evaluate governance and legal exposure. Check logging, access reviews, model testing, data-protection duties, intellectual-property restrictions, retention settings and procedures for notifying customers or regulators after an AI-related incident.
  8. Model frequency, severity and accumulation. Estimate direct loss, business interruption, restoration cost, liability and response expense. Then ask what happens if a common cloud, model, software component or managed service fails for many business units or insureds simultaneously.
Assessment lens Questions to answer Evidence to retain
Use case What does the system do, and can it act without approval? System inventory, workflow diagrams and permission lists
Data and model What data enters the model, and who trains, hosts or updates it? Data classification, provider terms and version records
Human control Who verifies a high-impact output, and what is the escalation path? Approval rules, training records and exercise results
Third party Which supplier outage or compromise would stop the process? Contracts, service-level terms and supplier assessments
Recovery Can the organization operate if the model, provider or data is unavailable? Offline procedures, backups and recovery-time tests

Which AI attack scenarios should be tested first?

Payment and account fraud

Combine a deepfake or generated email with stolen credentials and an attacker may bypass an otherwise mature technical perimeter. Require out-of-band confirmation using a known number or previously registered channel, dual approval for unusual payments and a documented exception process. Do not treat a familiar voice or video as authentication.

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AI-assisted vulnerability exploitation

Prioritize internet-facing assets, identity systems and software that process sensitive data. Continuous asset discovery, risk-based patching and compensating controls reduce the window in which automated scanning can become an incident. Record why a critical patch was delayed and what temporary protection was applied.

Data leakage through prompts and connectors

An employee can unintentionally send confidential text to a public model, or an AI agent can retrieve more data than its task requires. Enforce data-loss controls, least-privilege tokens, separate environments for testing and production, and logs showing prompts, retrieved records and outbound actions.

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Model or supplier compromise

A poisoned model, malicious update or provider breach can affect every workflow that trusts the same component. Maintain an inventory of model versions and dependencies, require supplier notification of material incidents, and keep a way to disable or replace the service without losing the underlying records.

Is AI making cyber losses more systemic?

It can. A single company may suffer an ordinary privacy breach, but a common cloud region, software component, model provider or managed service can create correlated losses across many companies at once. The Bank for International Settlements says AI, geopolitical tension and interconnected digital ecosystems are increasing the speed, scale, sophistication and systemic nature of incidents. This is an accumulation problem: an insurer must estimate not only each policyholder’s exposure but also how many policies depend on the same underlying service.

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Portfolio tests should include a provider-wide outage, a vulnerability in a widely deployed component, a coordinated deepfake campaign against a sector and simultaneous regulatory or notification costs. Vary outage duration, restoration capacity, geographic concentration and policy limits. A company that appears resilient in isolation may still be exposed through a supplier shared with its competitors.

What security controls do insurers expect before writing coverage?

Underwriters commonly look for controls that make an attack harder, limit its blast radius and provide credible evidence after an event. Requirements differ by insurer, industry, revenue and limit, but the following controls materially improve both security and underwriting defensibility:

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  • Phishing-resistant identity: Use multifactor authentication resistant to replay or push-bombing for administrators, remote access, email and cloud consoles. Remove dormant accounts and review privileged access regularly.
  • Least privilege and segregation: Separate duties for payment creation and approval. Restrict model agents and service accounts to the minimum data and actions required.
  • Patch and asset management: Maintain a current inventory, prioritize exploitable internet-facing weaknesses and document remediation exceptions.
  • Tested offline backups: Keep protected copies that an attacker cannot alter through ordinary administrator credentials. Test restoration and record actual recovery times.
  • Vendor and model inventory: Track cloud, software, data, model and managed-service dependencies, including subcontractors and notification obligations.
  • Logging and data-loss controls: Retain identity, administrative, model-use and transfer logs long enough to investigate. Alert on unusual exports, prompt activity and privilege changes.
  • Human verification and training: Teach staff how to challenge urgent requests, detect synthetic media and use an independent channel for payment or account changes.
  • Exercised incident response: Include legal, communications, technology, finance and insurers or brokers in a tabletop. Define who can shut down an AI workflow and how evidence is preserved.

These controls do not guarantee coverage or eliminate loss. They give an insurer evidence that the organization can prevent, detect and contain the scenarios described in its application.

Are insurers using AI to price and manage cyber policies?

Yes, insurers are applying AI and machine learning across underwriting, pricing, claims, fraud detection and operations, but adoption varies by line and company. An NAIC survey covering 2022–2025 found that 88% of 193 responding auto insurers, 70% of 194 home insurers, 58% of 161 life insurers and 92% of 93 health insurers were using, planning to use or exploring AI or machine learning. The European Insurance and Occupational Pensions Authority reported in 2025 that 50% of non-life insurers and 24% of life insurers were already using AI somewhere in the value chain.

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These figures include exploration and planned use, not only production systems, and they are not cyber-insurance-specific adoption rates. Insurers may use models to classify submissions, detect anomalous claims, estimate loss frequency or route cases to specialists. Human oversight remains important because training data can encode unfair outcomes, model performance can drift and an automated decision may be difficult to explain to a policyholder or regulator. The U.S. Treasury’s Under Secretary for Domestic Finance Nellie Liang described AI on 27 March 2024 as “redefining cybersecurity and fraud in the financial services sector.”

Does cyber insurance cover AI attacks or deepfakes?

Sometimes, but the answer comes from the policy wording and the facts of the loss, not from the label “AI.” A deepfake that causes a fraudulent transfer may fall under crime coverage, a social-engineering endorsement or no coverage at all. A ransomware event launched with AI may be treated like any other covered network-security incident, subject to the policy’s definitions, exclusions, limits and duties.

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Coverage question What to verify
Breach response Whether legal counsel, forensics, notification, credit monitoring and public-relations costs are covered, and whether a panel must be used
Business interruption Waiting period, qualifying network outage, dependent-system outage, restoration period and calculation of lost income
Extortion and recovery Ransom negotiation, payment restrictions, system restoration and sublimits
Fraudulent transfer Whether social-engineering or funds-transfer fraud is excluded, sublimited or placed under a separate crime policy
Liability Claims alleging privacy, network-security or content-related harm, plus contractual and regulatory limitations
Systemic events Any aggregation, common-cause, war, infrastructure or widespread-vulnerability language that could cap or exclude correlated losses

NAIC notes that terms vary and that social engineering or funds-transfer fraud may be excluded, sublimited or handled under separate crime coverage. Coordinate cyber wording with crime, property, technology errors-and-omissions and general-liability policies so the same event does not leave a gap or trigger conflicting consent and reporting requirements.

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How should buyers compare cyber insurance policies?

Obtain the full wording, endorsements and application—not just a summary of limits—and compare the following items side by side:

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  1. Covered events and whether a security failure, privacy event, human error and dependent-system outage are defined separately.
  2. Exclusions for social engineering, funds-transfer fraud, unapproved tools, war, infrastructure failure, widespread vulnerabilities and failure to maintain stated controls.
  3. Sublimits, retentions, waiting periods and aggregate limits for business interruption, extortion, restoration, fraud and liability.
  4. Reporting deadlines, consent requirements, panel restrictions and the consequences of using a vendor or lawyer outside the approved process.
  5. Security warranties, minimum-control representations, retroactive dates and how a material change in AI use must be reported.
  6. Systemic-event language, including how one provider failure is aggregated across related claims and whether a special cap applies.
  7. Coordination with crime, property, technology errors-and-omissions and general-liability policies.

Ask the broker or insurer to walk through a concrete deepfake payment, model-provider outage and ransomware scenario using the proposed wording. Require written answers for uncertain terms; a verbal assurance is not a substitute for an endorsement.

What market data shows about cyber-insurance pressure?

The U.S. market is expanding while claims and loss costs remain material. NAIC reported $9.46 billion in U.S. cyber direct written premium, 56,323 reported cyber-insurance claims and an industry loss ratio of 53 in 2025, up from 48.7. These are market-wide figures, not a measure of losses caused solely by AI, and the loss-ratio definition and reporting scope belong to the NAIC’s 2026 report.

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Official sources do not establish a validated percentage of cyber-insurance losses attributable only to generative AI. Treat AI as an emerging, compounding factor and document the specific attack mechanism, control failure, provider dependency and recovery cost in each event rather than assigning an unsupported AI-only loss ratio.

What should a board or risk committee ask now?

  • Which AI systems can access sensitive data, send external messages or approve consequential actions?
  • What happens if our main model or cloud provider is unavailable for a day, a week or longer?
  • Can finance independently verify a voice, video or email request to move money?
  • When was the last restoration test, identity review and incident-response exercise, and what failed?
  • Which policy responds to a deepfake-induced transfer, and what is the limit after the retention and sublimit?
  • How would we notify customers, regulators, suppliers and the insurer if a common provider affected many organizations?

Frequently Asked Questions

Can a deepfake payment scam be covered if no computer was hacked?

It may be covered by a crime or social-engineering endorsement, but a stand-alone cyber policy may exclude or sublimit it. Coverage usually turns on the policy’s funds-transfer definition, verification procedures, reporting deadline and any required dual-control process.

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Should small businesses buy cyber insurance if they use only basic AI tools?

Basic tools still create credential, data-leakage and supplier-dependency risks. A small business should first document its AI use, protect administrator accounts, maintain tested backups and verify payment requests, then purchase limits that match its likely interruption, response and liability costs.

What records make an AI-related insurance claim easier to prove?

Keep dated system and model inventories, access reviews, provider notices, identity and email logs, backup-test results, incident timelines and decisions made during containment. Preserve original messages or media so investigators can distinguish a deepfake, account takeover and ordinary payment error.

How often should an AI cyber-risk assessment be updated?

Review it whenever a model, provider, data source or autonomous permission changes, and at least annually for stable uses. Reassess immediately after a material incident, a major supplier outage or a new regulatory requirement.

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