Artificial intelligence is becoming embedded across business operations and informing decisions, workflows, and customer experiences. And it’s often happening faster than oversight can keep pace. As a result, legal and financial exposure is beginning to surface in ways leadership teams didn’t anticipate.

Recent legal developments and insurer attention are converting AI mistakes into real claims and coverage questions. For example, courts have begun allowing discrimination claims to proceed where plaintiffs allege automated hiring tools produced biased outcomes. In another case, an airline was required to honor incorrect information generated by its chatbot.

These examples don’t suggest organizations should avoid AI. Instead, they remind us that companies remain accountable for how automated systems influence decisions affecting people, customers, and operations. At the same time, insurers and regulators are paying closer attention to how organizations govern AI use.

Many companies assume AI exposure fits neatly within existing cyber coverage. In practice, AI introduces forms of risk that extend beyond traditional data breach scenarios.

Where AI Is Creating Exposure

AI risk rarely sits in one function. It spreads across people, technology, operations, and governance. In many organizations, usage spreads across teams before leadership has full visibility into where exposure is forming. Common areas include:

Employment and hiring decisions

Recruiting and screening tools can unintentionally favor or exclude candidates. Bias in training data or model logic can trigger discrimination claims. This exposure often intersects with Employment Practices Liability (EPL).

Data use and privacy

AI systems often rely on large volumes of data, including sensitive or proprietary information. Improper use, leakage, or model training on restricted data can create privacy and regulatory exposure.

Intellectual property risk

Model outputs may incorporate protected or proprietary material, raising questions around ownership and infringement.

Reliability and decision risk

AI-generated outputs can be inaccurate or misleading. When those outputs influence customer decisions, financial outcomes, or product performance, liability may follow, often intersecting with Technology Errors & Omissions (E&O).

Third-party dependency

Many organizations rely on external AI vendors. Their failures, bias, outages, or data issues can become your liability.

What Leaders Should Do Now

Managing AI risk starts with visibility and ownership. Organizations don’t need perfect governance to make meaningful progress, but they do need clarity on where exposure exists. Start here:

Build visibility

  • Map where AI is currently used across the organization.
  • Flag where automated outputs influence hiring, customer interaction, financial decisions, or product behavior.

Establish governance and accountability

  • Assign clear ownership for AI oversight.
  • Document baseline controls for data use, model review, and approval for higher-impact applications.

Address employment and bias exposure

  • Pressure test recruiting and screening tools for bias.
  • Clarify how algorithms evaluate candidates and which data they rely on.
  • Recognize that biased automated decisions can drive Employment Practices Liability (EPL) claims.

Evaluate third-party risk

  • Require vendor transparency, control attestations, and contractual protections.
  • Confirm where accountability for AI-driven outcomes remains with your organization.

Align insurance and risk strategy

  • Review how existing policies respond across Technology E&O, EPL, Cyber and Privacy, and D&O.
  • Identify exclusions, ambiguity, or emerging gaps.

These steps improve visibility and help organizations understand exposure before it becomes a claim, regulatory issue, or operational disruption. They also signal to underwriters that the company understands and manages AI risk.

Coverage Considerations

AI can create liabilities that traditional coverage wasn’t originally designed to address. As AI becomes more embedded in operations, leadership teams should understand how exposure may intersect with existing coverage, including:

  • EPL, particularly for automated hiring and screening.
  • Technology E&O, where AI-driven outputs shape customer decisions or business outcomes.
  • Cyber and Privacy, where data use, leakage, or model training creates exposure.
  • Directors & Officers (D&O), where governance and oversight of AI become leadership-level concerns

Coverage outcomes often depend on how risk is disclosed, governed, and documented.

Broker Checklist: AI Risk

Questions to ask your broker:

  • Does my current cyber policy cover AI-related exposures, or are there exclusions?
  • Are there AI-specific endorsements available for E&O or Tech Professional Liability?
  • How do carriers define “algorithmic liability,” and what triggers coverage?
  • Will regulatory fines related to AI compliance be covered under any policy?
  • What documentation will underwriters expect at renewal if we deploy AI in critical workflows?
  • Are insurers differentiating between proprietary AI models and third-party tools in underwriting?

What AI Risk Means for Leadership

AI will remain part of how organizations operate and grow. The task for leadership is ensuring that the organization understands where it introduces risk.

Companies that build visibility, assign ownership, and align protection early are better positioned as they scale. Those that delay often learn through claims, regulatory attention, or investor concern. Remember, the goal isn’t to slow adoption. It’s to advance with clarity and fewer surprises.

Understand Your AI Exposure Before It Becomes a Claim

If AI is already influencing decisions inside your organization, now is the time to understand how that exposure shows up in your risk profile. A review of governance, vendor dependencies, and insurance alignment can help surface gaps before they turn into claims or regulatory scrutiny. A Sequoia advisor can help take a closer look.

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Kristen Peed

Kristen Peed

is the Chief Risk Officer at Sequoia and has over two decades of experience in the risk industry. She leads the placement of Sequoia’s corporate insurance programs, including captive operations, enterprise risk management, and the Risk team. Kristen also serves as the 2025 President of the Risk Management and Insurance Society (RIMS) and was named one of Captive Review’s Top 20 Captive Owners for 2025. She earned her B.A. in Industrial Relations from the University of North Carolina at Chapel Hill. Outside of work, Kristen enjoys training for half-marathons with her dog, golfing with her husband, and going to the beaches of South Carolina.