Cyber Insurance MGAs: AI-Driven Policies That Fit

Cyber insurance keeps getting treated like a finance purchase. That’s the mistake.

When ransomware, supply-chain outages, and AI-assisted intrusion campaigns change tactics faster than annual renewals, a cyber policy can’t be a static document written from last year’s assumptions. The policies that hold up under modern cyber risk are the ones built from fresh threat data, realistic control expectations, and fast underwriting feedback loops.

That’s why cyber insurance MGAs (managing general agents) have become so influential—and why the next phase of cyber insurance underwriting is going to be tightly linked to AI in cybersecurity. If you’re a CISO, risk leader, or CFO buying coverage, the real question isn’t “carrier vs. MGA.” It’s: who can correctly measure your cyber risk today, and who can keep up with it six months from now?

What a cyber insurance MGA really does (and why it matters)

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. In practice, MGAs can look like insurers from the outside because they’re the ones asking for security details, setting terms, and sometimes even helping manage claims.

The value MGAs bring is focus. Cyber risk doesn’t behave like property risk, where decades of actuarial history and stable loss patterns make underwriting more predictable. Cyber underwriting requires technical judgment and an ability to interpret messy signals: security control maturity, exposure data, threat actor behavior, third-party dependencies, and incident response readiness.

Here’s the stance I’ll take: cyber insurance works better when underwriting is closer to the security reality CISOs live in. MGAs often operate “closer to the ground,” with quicker underwriting cycles and more current threat intelligence. That tends to produce policy language and requirements that match how attacks actually happen.

Why CISOs should care (even if procurement runs insurance)

Many organizations still buy cyber insurance through a CFO-led procurement flow. The CISO gets pulled in late—sometimes only when a questionnaire lands in their inbox.

That approach is backwards. Underwriting is now a security review. MGAs in particular are incentivized to understand your controls because their economics depend on controlling losses. If the buyer treats the process as transactional, they lose a chance to:

• Explain why a control is effective in their environment (not just that it exists)
• Negotiate terms based on demonstrable maturity
• Avoid exclusions that quietly gut the policy
• Align incident response requirements with how the SOC actually operates

The upside: when your program is strong, MGA scrutiny can translate into broader coverage, better terms, or more stable pricing.

The underwriting shift: from “checkboxes” to measurable security posture

Underwriting used to resemble a long list of yes/no questions. That’s fading fast.

A modern cyber risk assessment is moving toward evidence-based posture measurement—and AI is a major reason. Underwriters increasingly want proof of controls working, not a promise that a tool is deployed.

Controls MGAs tend to scrutinize hardest

If you’re preparing for renewal or shopping for coverage, expect deep attention on controls tied to ransomware, privilege abuse, and recovery:

• Multifactor authentication (MFA) (especially for remote access and admin access)
• Endpoint detection and response (EDR) coverage and alert handling
• Privileged access governance (PAM, just-in-time access, admin separation)
• Network segmentation for critical systems and backups
• Backup and disaster recovery testing (frequency, immutability, restoration time)

Notice what’s different: these aren’t theoretical controls. They’re the controls that decide whether an incident becomes a minor disruption or a multi-week outage.

Where AI changes the underwriting game

AI can help MGAs move from rough proxies to more accurate risk signals—without turning underwriting into a months-long audit.

Practical examples of AI in cyber insurance underwriting that are already emerging across the market:

1. Automated evidence extraction: AI systems can parse logs, configurations, and policy documents to confirm control presence and scope.
2. Anomaly detection on posture: instead of “EDR deployed: yes,” models can flag gaps (like unmanaged endpoints or unusual admin behavior) that correlate with loss.
3. Threat-informed scoring: AI can map your industry and tech stack against active attack patterns to estimate likelihood of certain incident classes.
4. Continuous monitoring models: some MGA approaches are shifting from annual snapshots to posture signals that update over time.

This matters because cyber risk doesn’t change annually—it changes weekly.

Faster, smarter policy design: why MGAs often lead innovation

MGAs exist because carriers can’t be experts in every niche. In cyber, that specialization shows up in policy design and operational detail.

A well-run cyber MGA tends to innovate in three areas that directly affect insureds.

1) Coverage design that reflects real-world incidents

Policy wording is where good intentions go to die.

MGAs often put more energy into definitions and scenarios that cause claims disputes, such as:

• What counts as a “security failure” versus “human error”
• How “system failure” interacts with cloud outages and SaaS dependency
• How third-party incidents (vendors, MSPs, supply chain) are covered
• Where “cyber war” or “state-backed” exclusions begin and end

The stronger MGAs don’t just write better language—they pressure-test it against how incident response actually unfolds.

2) Incident response panels curated for speed

When a breach hits, you don’t want to discover your policy only pays for firms you’d never hire.

MGAs are often active in curating incident response panels (legal, forensics, negotiation support, PR) because response quality controls loss severity. That’s an underwriting decision, not just a service add-on.

3) Underwriting cycles that match business reality

If you’re mid-market and trying to close deals, renew contracts, or satisfy customer security requirements, you don’t have time for an underwriting process that drags.

MGAs often compete on cycle time—faster decisions, more flexibility on “tough-to-place” risks, and more willingness to negotiate when security leadership can explain context.

The blurry line: when the underwriter also sells security tools

A growing number of MGAs bundle cybersecurity services—anything from risk scanning and posture dashboards to MDR-like monitoring and incident response subscriptions.

This can be genuinely useful. It can also create a trust problem.

Done right, bundling creates alignment between risk mitigation and risk transfer. Done poorly, buyers can’t tell if they’re being protected or profiled.

How to evaluate bundled tools without getting burned

Use a simple test: does the tool measurably reduce loss impact, or does it mainly produce underwriting inputs? Both can be valid, but you should know which you’re buying.

Ask these questions during the buying process:

• What decisions will your data influence? Pricing? Retention? Coverage limits? Exclusions?
• Is the telemetry shared with the carrier, the MGA, or both?
• Can we see the scoring logic or the main drivers? If the output is a black box, expect surprises later.
• What’s the operational burden? If it generates alerts, who triages them?
• Is there a real control improvement path? Or just a dashboard with a “risk grade?”

My opinion: if the bundled offering doesn’t come with a clear operational workflow, it becomes shelfware—and you’ll still pay for it, one way or another.

A practical playbook for CISOs: getting better terms with AI-ready evidence

CISOs can materially influence cyber insurance outcomes, especially with MGAs, but only if they show up with the right artifacts.

Step 1: Treat underwriting as a security architecture review

Bring evidence that connects controls to outcomes:

• MFA coverage reports (by application class)
• EDR coverage maps (including servers and remote endpoints)
• Backup architecture diagrams plus last restoration test results
• PAM deployment scope and admin workflow
• Network segmentation approach for crown jewels and backup networks

If you can’t produce these quickly, that’s not an insurance problem—it’s a security operations maturity problem.

Step 2: Use AI to package evidence at the speed underwriting needs

Security teams often have the data, but it’s scattered across tools.

Where AI helps is summarizing and normalizing evidence:

• Generate control narratives from configuration exports and SOPs
• Summarize patch and vulnerability trends by business unit
• Convert incident metrics into underwriting-friendly summaries (MTTD, MTTR, containment time)
• Map controls to common cyber insurance questionnaires automatically

The goal isn’t to impress underwriters with buzzwords. The goal is to reduce friction so the underwriter spends time understanding your posture, not chasing missing documentation.

Step 3: Negotiate the parts that actually matter

Premium gets attention. Exclusions and sublimits quietly decide whether the policy pays.

Focus negotiation on:

• Ransomware sublimits and conditions
• Business interruption triggers (especially cloud/SaaS dependencies)
• Third-party and supply-chain event coverage
• Incident response vendor choice and pre-approval requirements
• Retroactive dates and failure-to-maintain clauses

If you have strong controls, you’re in a position to push for policy language that matches your real risk profile.

Where cyber insurance is heading in 2026: continuous underwriting

Annual renewal cycles are too slow for cyber risk. The industry is drifting toward continuous underwriting, where posture signals update throughout the year.

AI is the only realistic way this scales.

Expect these shifts to accelerate:

• From static questionnaires to continuous control validation
• From broad industry pricing to environment-specific pricing
• From point-in-time assessments to trend-based risk scoring (improving posture should matter)
• From generic loss prevention advice to targeted mitigation plans tied to likely attack paths

For the “AI in Insurance” series, this is a pivotal moment: cyber insurance underwriting is becoming a cybersecurity analytics problem. The MGAs that win will be the ones that can combine threat intelligence, security telemetry, and operational expertise into policies that both price risk correctly and actively reduce it.

The lead question to keep on your whiteboard: If your cyber posture improved by 30% in the last six months, would your policy recognize it—or would you still be priced like last year?