Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

Vendor assurance efforts are increasing, but risk leaders don’t trust the results of that effort. In KPMG’s Global Third-Party Risk Management (TPRM) Survey, only 15% of risk leaders said they have high confidence in the data that underpins their TPRM program. Only 17% rate their data quality as excellent. Security teams are running more assessments and sending more questionnaires than ever, but fewer than one in five leaders trust what any of that produces.

Enterprise security teams sit on enormous volumes of operational data. Vulnerability scanners produce thousands of findings weekly. Endpoint agents generate millions of events daily. SIEM platforms ingest logs from every system in the environment. Threat intelligence feeds fire off indicators by the hour. All of this data is useful for operational security work.

Effective cyber risk quantification means moving past subjective heatmaps and translating technical vulnerabilities into dollar-denominated loss exposure and probability distributions that the CFO, board, and cyber insurance underwriter can act on. It is the discipline that turns cyber from a technical cost center into a strategic risk portfolio managed alongside every other category of enterprise exposure.

AI agent sprawl is the uncontrolled proliferation of AI agents, autonomous assistants, and LLM-powered tools across an organization without centralized tracking or governance. It mirrors historical IT challenges like SaaS sprawl and shadow IT, and it emerges when decentralized business units build or deploy agents independently, without coordinated oversight from security, IT, or risk teams. ‍ The difference is that these agents are active software actors.

Governing AI agents at enterprise scale requires a fundamental change in how security, risk, and compliance teams think about AI oversight. The generative AI era focused governance on output quality: what the model says, what it produces, and whether the content meets policy standards. ‍ The agentic era demands governance of action and delegated authority: what the AI is allowed to do, what systems it can touch, and how its decisions trace back to human accountability.

Enterprise buyers evaluating AI agent security in 2026 face a market that has fragmented into specialized categories, each solving one layer of the problem well and other layers poorly. Identity vendors govern non-human credentials. Runtime vendors constrain what agents can do at the moment of execution. Established security platforms extend their existing offerings into the agentic space. ‍

‍Cyber risk quantification (CRQ) has moved from optional to operational in financial services. The average cost of a data breach in the sector reaches $5.56 million, and regulatory mandates including DORA, NYDFS Part 500, and SEC cyber disclosure rules demand quantified, defensible loss exposure figures the finance function can act on. ‍

AI-related security incident detection starts with knowing what AI systems are running across the organization. Without a complete, continuously updated inventory of sanctioned, shadow, and third-party AI tools, security teams cannot detect incidents involving systems they do not know exist. From there, effective incident management requires a structured response framework that connects detection to containment, investigation, remediation, regulatory notification, and governance integration. ‍

Cyber risk quantification (CRQ) models are as accurate as the data and methodology behind them, and the conversation about CRQ accuracy that plays out across security and finance teams is often stuck on the wrong question. Risk is about future events that may or may not happen, and if they do, the impact will vary. ‍ Looking for certainty in a probabilistic model is a category error. The useful question is not whether a CRQ model produces the "right" number.