Cybersecurity has become one of the most operationally critical managed services categories for insurers, because the industry combines high-value financial workflows with sensitive data. As a result, insurers are now increasingly focused on cybersecurity run ownership: 24×7 coverage, faster containment, audit defensibility, and resilience under disruption.
This matters for service providers because cybersecurity is now a meaningful growth lever inside overall insurance services outsourcing. In CY24, insurance cybersecurity managed services (CMS) spend was ~US$2.4-2.7 billion, and is growing at 11–13% CAGR (2025–27), making it a structurally attractive segment to win and expand.
A market split: mid-market insurers buy “packaged run,” while Tier-1 insurers buy “co-managed scale”
Across insurers, the buying motion is increasingly segment-driven:
- Mid-market insurers (DWP = $1-10 billion) tend to contract fully Managed Security Operations Center (SOC) / Managed Detection and Response (MDR) first because alert volumes and staffing constraints exceed internal capacity. They prefer predictable, bundled constructs where the provider supplies the tools + people + run processes, and proves day-to-day operational ownership and responsiveness.
- Tier-1 insurers (DWP > $10 billion) usually prefer co-managed operating models where the provider scales execution (often L1–L3, sometimes L4) across complex, global environments, while the insurer retains governance, risk acceptance, approvals, and key architectural decisions. They value ecosystem-scale integration, continuous improvement in detection/response, and modernization velocity.
This split shapes where demand clusters across cybersecurity segments. Threat management and identity are consistently high-demand anchors, while cloud security and application security often drive incremental growth as attack surfaces expand and modernization continues. In the broader cybersecurity services mix, demand concentration also tracks where spend and growth are strongest (such as IAM, cloud security, threat management, and application security), while categories such as endpoint security and network security tend to be smaller share segments.
AI is reshaping the insurer threat landscape, and it challenges “traditional” security assumptions
Insurers already have Artificial Intelligence (AI) models, data pipelines, and AI agents in production, which introduces new internal cyber risks. They need operational guardrails that continuously govern how AI systems behave, how decisions are made, and how risk is observed and reported.
The insurer threat response: telemetry to automated defense
The multi-layered CMS operating model is a closed-loop system that converts insurer telemetry into faster, more reliable response:
Insurer telemetry (logs + events) flows into:
- Security Information and Event Management (SIEM) to collect, normalize, and correlate logs into alerts with context
- User and Entity Behavior Analytics (UEBA) to detect behavior and anomaly signals that elevate risk
These signals feed the SOC, where analysts triage, investigate, and decide actions, which then drive Security Orchestration, Automation, and Response (SOAR) that executes standardized playbooks to automate containment and remediation steps. Critically, outcomes from resolved incidents feed back into tuning (SIEM rules are refined and UEBA baselines are adjusted) improving future detection quality and reducing noise over time.
Cyber-for-AI is emerging as the next managed services wedge, but it is still maturing
In practice, the early demand for cyber-for-AI is coupled with AI-for-cyber and concentrating around governance-led services such as:
- Preventing leakage and over-sharing of sensitive data through AI interactions
- Controlling model/agent access (including API keys, service accounts, and permissions)
- Monitoring for abnormal model/agent behavior and unsafe outputs
- Addressing risks like prompt injection and poisoning through guardrails and continuous oversight
Currently, much of this work is still assessment- and consulting-led, because the ecosystem lacks fully standardized/productized, “drop-in” tooling for many AI-specific risks. But the trajectory is clear: as AI capabilities move deeper into insurer production environments, cyber-for-AI is becoming a leading line item in managed security conversations.
Outcomes are increasingly tagged to contracts
Insurers are starting to evaluate CMS success through outcomes that map to business risk and operational resilience, not just response-time SLAs. While delivery remains SLA-led in many cases, a notable shift is emerging in Tier-1 contracts: fixed-fee constructs with a performance premium, where a premium portion is unlocked if the provider delivers agreed improvements within defined bands (for example, severity score improvement, incident reduction, and other insurer defined reliability metrics).
This does not yet mirror the scale or maturity of outcome-based modernization deals, but signals a steady move toward commercial models that reward measurable operational impact and reliability under stress.
Where this leaves the market?
The competition landscape is sharpening around who can credibly run the “always-on” cyber operating model for insurers (across people, platforms, and closed-loop tuning), while adapting controls for AI-era risk. Providers that can demonstrate operational continuity, compliance coverage, and repeatable response quality (in both fully managed and co-managed constructs) will be best positioned to gain durable share.
If you enjoyed this blog, check out, Cybersecurity services outlook for 2026 – from evolution to reinvention – Everest Group Research Portal, which delves deeper into another topic relating to cybersecurity.
If you would like to benchmark how Tier-1 versus mid-market insurers are contracting CMS today, and how cyber-for-AI is likely to reshape provider differentiation next – please reach out to Ronak Doshi ([email protected]), Aurindum Mukherjee ([email protected]), and Faisal Arfeen ([email protected]) to discuss in depth view of demand patterns, competition dynamics, and what “good” looks like across the operating model.