Cyber Insurance MGAs Are Rewriting the Risk Playbook

Most companies still buy cyber insurance like it’s a finance checkbox: get quotes, pick a limit, negotiate price, renew next year. That approach is starting to break—because cyber risk doesn’t sit still long enough for traditional insurance cycles.

That’s where cyber insurance managing general agents (MGAs) are making a real difference. They’re not just “another insurer.” They’re often the group pushing policy language, underwriting expectations, and incident response requirements to keep pace with ransomware tactics, cloud misconfigurations, supply-chain blowups, and (increasingly) AI-enabled attacks.

This post is part of our AI in Insurance series, and I’ll take a stance: MGAs are becoming the bridge between AI-driven cybersecurity realities and the insurance contracts that are supposed to fund recovery. If you’re a CISO, a risk leader, or a CFO trying to stop premium spend from turning into a bad surprise at claim time, you’ll want to understand how MGAs operate—and how to work with them.

What cyber insurance MGAs actually do (and why it matters)

A cyber insurance MGA is an intermediary that designs, underwrites, and administers policies on behalf of an insurance carrier. The carrier holds the balance-sheet risk. The MGA does much of the “thinking work”: screening applicants, defining control requirements, shaping exclusions, and sometimes participating in claims handling.

Here’s the practical implication: an MGA can change what “good security” means in underwriting faster than a large carrier can. That speed matters in cyber, where the threat landscape can shift in a quarter—sometimes in a weekend.

Why the MGA model fits cyber risk

Cyber insurance has a structural problem: limited actuarial history and inconsistent security telemetry compared to lines like property or auto. Underwriters can’t rely on decades of stable loss data because both the attackers and the tech stack change constantly.

MGAs exist precisely for markets like this. They specialize in areas where:

• Risk is technical and fast-changing
• Data is messy or incomplete
• Standard policy forms lag behind reality
• Underwriting needs subject-matter expertise, not just tables

For buyers, that specialization often shows up as tighter control validation and more modern policy language—especially around ransomware, third-party outages, and incident response.

Why MGAs are pushing underwriting toward “security proof,” not “security promises”

Cyber underwriting has moved from “tell me you have antivirus” to “prove your controls operate under pressure.” MGAs are a big reason for that shift.

In practice, many MGA-driven applications and renewals will ask CISOs to demonstrate controls such as:

• Multifactor authentication (MFA) coverage (especially for admin access)
• Endpoint detection and response (EDR) deployment and monitoring
• Privileged access management (PAM) and governance
• Network segmentation for critical systems
• Backups that are immutable/offline and regularly tested
• Disaster recovery testing with real recovery time objectives

This isn’t bureaucracy for its own sake. It’s loss control. The insurer’s economics depend on it.

Where AI shows up in underwriting (even when no one calls it “AI”)

A lot of the “new MGA underwriting” is powered by analytics and automation that looks a lot like the AI used in modern security programs.

Common examples include:

• Automated security posture signals (for example, exposure indicators, patching signals, external attack surface observations)
• Risk scoring models that prioritize which applicants get deeper review
• Threat intelligence enrichment to interpret what a given exposure means right now
• Faster triage of supplemental questionnaires based on predicted loss likelihood

The bridge to the campaign angle is straightforward: policy adaptation mirrors AI-based security operations. Both are trying to do the same thing—process messy, changing signals quickly enough to prevent (or at least reduce) damage.

The underrated benefit for CISOs: MGAs can price good controls more accurately

The best outcome in cyber insurance isn’t “getting a policy.” It’s getting a policy whose terms match your real posture, so that:

• You’re not paying for risk you’ve already mitigated
• You’re not denied coverage because your environment doesn’t match what the application implied
• You can negotiate endorsements from a position of evidence

MGAs are often closer to the technical details and more willing to evaluate nuanced control maturity. That can work in your favor if your program is strong.

A concrete example: ransomware resilience and coverage terms

Ransomware is where underwriting expectations get painfully specific. Consider two midmarket firms with similar revenue and headcount:

• Company A has MFA “for most systems,” inconsistent local admin control, and backups that haven’t been restoration-tested in 12 months.
• Company B has enforced MFA for all remote and privileged access, EDR with 24/7 monitoring, segmented backups, and quarterly restore tests.

On paper, both “have MFA and backups.” In practice, their risk profiles are wildly different.

MGAs that use more granular controls assessment (often supported by automated evidence collection or structured scoring) are more likely to differentiate these two companies with:

• broader ransomware coverage or fewer carve-outs
• lower retentions for certain event types
• better incident response panel options
• more stable renewal pricing

If you’re a CISO, this is one of the few times you can convert real security maturity into a financial artifact the business understands.

When MGAs bundle security tools, the incentives get complicated

Some cyber MGAs don’t just underwrite—they also bundle or resell security capabilities (MDR-like services, assessments, dashboards, training, incident response retainers). This can be helpful, but it creates a legitimate question:

Are you being protected, or profiled?

The “alignment” case (when it works)

Bundled services can create tight feedback loops:

• The MGA incentivizes controls that reduce loss frequency and severity
• The insured gets practical help closing gaps
• Underwriting becomes less guessy because controls are observable

In the AI context, this is where continuous control monitoring starts to matter. Instead of a once-a-year questionnaire, the model trends toward ongoing signals—similar to how AI-driven security monitoring works.

The “conflict” case (when it backfires)

Bundling can also create friction:

• Security leaders worry about surveillance or misuse of telemetry
• Buyers may feel forced into a tool choice to get acceptable terms
• Data ownership and retention can become unclear
• It blurs accountability during an incident (“your tool didn’t alert” vs “your team didn’t act”)

My take: bundled security can be fine, but only with explicit boundaries. If your broker or MGA can’t clearly explain what data is collected, how it’s used in underwriting, and what happens during claims, treat that as a risk.

MGA vs. traditional carrier: how to decide what fits your org

The cleanest way to decide is to start with your operating reality, not the logo on the policy.

MGAs tend to be a strong fit when…

• You’re small to midmarket and need underwriting that understands modern stacks
• You want faster underwriting cycles and less “committee delay”
• You have a mature program and want it reflected in pricing and terms
• You need flexibility for a “tough-to-place” risk profile (industry, claims history, complex third parties)

Traditional carriers tend to be a strong fit when…

• You’re very large and need balance sheet capacity at higher limits
• Your program is highly standardized and you can tolerate slower cycles
• You have internal risk/insurance expertise and established relationships

In reality, many organizations blend both via layered programs. The important point is this: an MGA-written policy isn’t automatically better—but it’s often more responsive to current attack patterns.

How to work with MGAs without turning insurance into a second SOC

You’ll get the most value when cyber insurance purchasing stops being purely CFO-driven and becomes a joint effort between security, risk, legal, and finance.

1) Bring your CISO evidence package, not a narrative

Underwriters respond to proof. A lightweight evidence package usually beats a 20-page story.

Include:

• MFA enforcement scope (who/what is excluded and why)
• EDR coverage percentage and monitoring model (internal vs MDR)
• Patch SLAs and exception handling
• Backup architecture and last restore test date/results
• Network segmentation diagram (high-level is fine)
• Incident response plan and tabletop cadence

If you’re using AI in cybersecurity (SIEM correlation, UEBA, automated triage, SOAR), translate that into underwriting language: mean time to detect, mean time to contain, alert volumes, and how you prevent false positives from drowning analysts.

2) Ask MGA-specific questions that reduce claim ambiguity

These questions surface how modern (and fair) the policy really is:

• What exact events trigger ransomware coverage, and what exclusions commonly apply?
• How is “failure to maintain minimum security standards” interpreted at claim time?
• What telemetry (if any) is used for underwriting, and is it used during claims investigations?
• Which incident response firms are on the panel, and can we pre-negotiate rates?
• How are third-party and supply-chain events covered (including cloud/SaaS outages)?

3) Treat underwriting as a security roadmap—carefully

Underwriting scrutiny can highlight gaps worth fixing. But don’t let it become a random checklist.

A good rule: only prioritize underwriting-driven controls that also reduce real blast radius. MFA, EDR, privileged access governance, segmentation, and tested backups do.

If an MGA pushes a control that doesn’t change your incident outcomes, challenge it.

Where this is heading in 2026: continuous underwriting powered by AI

The direction is clear: cyber insurance is moving from annual snapshots to continuous underwriting, where risk signals update more frequently and policies adjust through endorsements, pricing changes, or coverage conditions.

AI is the enabler on both sides:

• Security teams use AI to detect anomalies, automate triage, and prioritize vulnerabilities.
• Insurers and MGAs use AI to interpret posture signals, forecast loss probability, and pressure-test controls against current threats.

That convergence is uncomfortable—but it’s also practical. Static questionnaires can’t keep up with attacker innovation, and MGAs are among the first to operationalize that reality.

If you’re serious about reducing cyber insurance surprises, the next step is to align three things that are too often separated: your security telemetry, your risk narrative, and your policy language. That’s the work that turns insurance from “paper” into recovery capability.

If you’re evaluating MGA-written cyber insurance or preparing for renewal, what would change your outcome more: tightening policy wording—or improving the evidence you can produce about how your controls perform under real attack pressure?