Cyber Insurance MGAs + AI: Underwrite Smarter Risk

Budget season has a funny way of exposing how companies actually think about cyber risk. Security leaders are asked to prove ROI, finance wants predictability, and the business wants growth without surprises. That’s exactly why cyber insurance keeps showing up in board conversations — not as a “nice to have,” but as a financial backstop.

Here’s the part most companies get wrong: they treat cyber insurance as a procurement exercise and AI security as a tooling decision. In practice, they’re two halves of the same risk program. Insurance transfers financial risk after an incident; AI reduces the probability and blast radius before it happens. When you connect those dots, you make underwriting easier, improve coverage outcomes, and get fewer nasty exclusions.

This post is part of our AI in Insurance series, and it focuses on a group that quietly influences what cyber coverage looks like in 2025: cyber insurance MGAs (Managing General Agents). They’re shaping policy forms, underwriting expectations, and even the security controls insurers expect — increasingly with AI-informed assessment models.

What a cyber insurance MGA really does (and why it changes your policy)

A cyber insurance MGA is an intermediary that designs, underwrites, and administers policies on behalf of an insurance carrier, which ultimately holds the risk on its balance sheet. To an outside buyer, MGAs can look like insurers — but they’re closer to a specialized underwriting and operations engine.

Why that matters: cyber risk doesn’t behave like property risk. There’s limited long-term actuarial history, attack methods shift quickly, and the same vulnerability can hit thousands of organizations in a week. MGAs exist because they can operate closer to the technical reality.

Faster underwriting cycles, more technical scrutiny

Compared with traditional carriers, cyber MGAs often:

• Underwrite faster (because their workflows are purpose-built for cyber)
• Use fresher threat intelligence (rather than relying on slow-moving loss data)
• Scrutinize security controls deeply (not just a checkbox app)
• Take on “tough-to-place” risks that large carriers may decline by default

If you’ve ever felt that an insurance application was oddly disconnected from how attacks work, MGAs are one reason that’s changing. When the underwriting team actually speaks the language of identity, endpoint telemetry, segmentation, and backups, the policy you get tends to map better to the risks you’re living with.

Why CISOs should care: MGAs pull insurance out of the CFO-only lane

Cyber insurance purchasing has historically been finance-led. That’s understandable — it’s a financial product. But it’s also a product whose pricing and coverage hinges on operational security reality.

When CISOs aren’t involved, companies end up with:

• Coverage that doesn’t match the organization’s real exposure (cloud, SaaS, OT, supply chain)
• “Surprise” exclusions discovered during an incident
• Underinsurance due to misunderstood business interruption scenarios
• Weak negotiation position because controls can’t be explained or evidenced

Cyber MGAs tend to want the CISO in the room because they’re motivated by loss ratios. They commonly expect proof of controls such as:

• Multifactor authentication (MFA) (often with an emphasis on phishing-resistant methods)
• Endpoint detection and response (EDR) and alerting maturity
• Privileged access governance (PAM) and admin workflow controls
• Network segmentation for critical systems
• Backups and disaster recovery testing with evidence of restore success

Done right, this scrutiny isn’t punitive. It’s negotiable leverage. A mature program can earn broader coverage, more stable premiums, and fewer coverage carve-outs.

A useful mindset shift: treat underwriting as a structured security review that can lower your total cost of risk.

The blurry line: when your underwriter also sells security tools

A growing number of cyber MGAs bundle technology services — sometimes full managed detection and response, sometimes continuous risk scanning, sometimes incident response subscriptions, sometimes training. This creates a real advantage and a real buyer trap.

The upside: alignment between risk mitigation and risk transfer

Bundled controls can reduce claims frequency and severity. That can benefit both sides:

• The MGA lowers loss exposure
• The insured improves detection and response outcomes
• Underwriting becomes evidence-driven instead of questionnaire-driven

If you’re a small or midmarket organization, these bundles can also shortcut a tooling gap: you may not have a 24/7 SOC, mature telemetry, or enough staff to operationalize detections.

The downside: “Are you protecting me, or profiling me?”

When the same organization is assessing you for premium and offering you the tool that improves your assessment, incentives can get messy.

If you’re evaluating an MGA that bundles security tech, push for clarity on:

• Data boundaries: What telemetry is collected? How long is it retained? Who can access it?
• Decision transparency: Which signals affect premium, renewal, limits, and exclusions?
• Portability: If you leave the MGA, do you keep the security data, detections, and response playbooks?
• Incident posture: Does using their tool change claims handling expectations or coverage conditions?

This isn’t a reason to avoid bundled offerings. It’s a reason to treat them like any other security vendor relationship — with contract terms, data governance, and operational ownership spelled out.

Where AI fits: better risk assessment, better policies, fewer surprises

AI shows up in cyber insurance in two places that matter to buyers:

1. Underwriting/risk scoring: assessing your exposure and control strength
2. Security operations: reducing your likelihood of a claim

The magic isn’t “AI everywhere.” It’s AI where it produces measurable signals.

AI-driven underwriting is only as good as the evidence

Traditional cyber underwriting leaned heavily on self-attestation: forms, checklists, and occasional scans. That model breaks under modern conditions — especially with SaaS sprawl and third-party dependencies.

AI-assisted underwriting is trending toward:

• Continuous external attack surface signals (domains, exposed services, misconfigurations)
• Control validation signals (MFA posture, endpoint coverage, patch velocity)
• Probabilistic loss modeling (how certain combinations of gaps correlate to incident severity)

But here’s the uncomfortable truth: AI scoring can penalize you if you can’t produce clean evidence. If your IAM reporting is messy, asset inventory is incomplete, or endpoint coverage is unknown, you’ll look riskier than you might be.

AI in the SOC reduces the insurer’s real concern: dwell time

Underwriters worry about frequency, severity, and time-to-containment — because those drive claim size. AI helps when it improves:

• Detection of identity anomalies (impossible travel, token abuse, privilege escalation)
• Correlation across noisy telemetry (EDR + cloud logs + email + SaaS)
• Faster triage and case enrichment (what happened, to whom, and how far it spread)

If you want a practical North Star for aligning AI security investment to insurance outcomes, it’s this: prove you can detect and contain fast, and back it up with logs and exercises.

Practical playbook: how to approach MGA underwriting with an AI-first posture

If your renewal is coming up in Q1 (common for many organizations after year-end planning), you can make this process materially easier with a few targeted moves.

1) Build an “underwriting evidence pack” (not a slide deck)

Most teams show underwriters policies and architecture diagrams. Helpful, but not enough. What moves pricing and terms is proof.

Include:

• MFA coverage report (who’s exempt and why)
• EDR coverage report (endpoints covered / total endpoints)
• Backup testing evidence (restore screenshots/logs, RTO/RPO results)
• IR tabletop schedule + after-action summaries
• Vulnerability remediation metrics (patch SLAs met vs missed)

If you’re using AI-assisted detection or triage, document:

• Which use cases are live (phishing triage, identity anomaly detection, alert correlation)
• Mean time to acknowledge/contain for priority scenarios
• How humans review and approve actions (especially if any automation is involved)

2) Treat “control expectations” as negotiable contract inputs

MGAs commonly require specific controls. Don’t accept vague language like “industry standard security” or “appropriate controls” without clarification.

Ask for specificity:

• What exactly counts as MFA for privileged users?
• Are break-glass accounts allowed, and under what conditions?
• Is “tested backups” annual, quarterly, or after every major system change?
• Is EDR required on servers, endpoints, or both?

Clear answers reduce claim disputes later.

3) Use AI to tighten third-party and supply chain narratives

Third-party risk is where many cyber policies become painfully narrow. AI can help you get sharper and more credible:

• Identify critical vendors by access pathways (SSO, API tokens, admin roles)
• Monitor SaaS configuration drift (M365, Google Workspace, identity providers)
• Flag abnormal vendor access patterns

Bring that visibility to underwriting. It demonstrates governance, not just intent.

4) Don’t let the CFO be the only voice — but also don’t sideline finance

The best outcomes happen when the CISO and CFO present a shared story:

• The CFO frames risk tolerance, limits, and retention
• The CISO proves control maturity and incident readiness
• The broker translates both into terms, endorsements, and pricing

If you’re a security leader, your job isn’t to “buy insurance.” It’s to ensure the policy matches the organization’s real operational posture.

What to ask an MGA (and what their answers tell you)

When you’re choosing between an MGA-driven policy and a traditional carrier policy, these questions surface the difference fast:

1. “How do you validate controls — questionnaire, scans, or telemetry?”

   • Telemetry-based answers usually mean faster renewals and fewer surprises.

2. “What incident response panel options do we have, and can we keep our preferred firms?”

   • Flexibility here matters under real stress.

3. “How do you define systemic events and cyber warfare exclusions?”

   • You want clarity, not broad carve-outs.

4. “If you bundle security tools, what data is used for underwriting decisions?”

   • Look for transparent boundaries and retention limits.

5. “What’s your stance on ransomware negotiation and payment?”

   • It impacts response speed, legal posture, and claim handling.

Strong MGAs will answer clearly. Weak ones will hide behind generalities.

The stance I’ll take: insurance is necessary, but prevention earns the real savings

Cyber insurance is a financial instrument. It doesn’t stop intrusions, contain ransomware, or clean up identity sprawl. What it can do is keep a bad week from turning into a multi-year financial wound.

Cyber insurance MGAs are pushing the market toward tighter control expectations and more technically informed policy design. That’s a good thing — especially for CISOs who can prove maturity. Pair that with AI-driven threat detection and response, and you get the result leadership actually wants: fewer incidents, smaller claims, better coverage terms, and clearer renewals.

If you’re planning your 2026 risk program right now, consider this your litmus test: Can you explain, with evidence, how AI reduces your likelihood of a claim — and can your policy keep up with how your environment actually works?