CrowdStrike Named a Leader in The Forrester Wave™: Proactive Security Platforms, Q3 2026

CrowdStrike was named a Leader in The Forrester Wave™: Proactive Security Platforms for Q3 2026, receiving the highest Strategy category score and maximum scores in the Innovation and Roadmap criteria. This recognition reflects a market shift where proactive security moves beyond traditional vulnerability management to focus on understanding asset risk, adversary exposure, and prioritization of remediation.
CrowdStrike addresses this evolution through Falcon Exposure Management, which unifies exposure data with threat intelligence, exploit prediction, and attack path analysis. This unified approach allows teams to prioritize and act faster by combining visibility, prioritization, validation, and remediation within the same AI-native platform. Specific innovations include ExPRT.AI for exploit prediction, Attack Path Analysis, Asset runtime enumeration, and Compensating control awareness. Furthermore, features like Charlotte AI and AgentWorks enable the automation of workflows for risk assessment and remediation through no-code agents.
The evolution described is a shift from merely collecting attack surface management data to modernizing prioritization into actionable steps. The technology aims to reduce the time between discovering an exposure and mitigating its risk by integrating context across security operations, enabling analysts to focus on action rather than evidence assembly.

* CrowdStrike was named a Leader in The Forrester Wave™: Proactive Security Platforms for Q3 2026.
* The evaluation showed the highest Strategy category score.
* The evaluation showed maximum scores in the Innovation and Roadmap criteria.
* Exposure management moves from attack surface management to exposure management, prioritization, and action.
* CrowdStrike Falcon® Exposure Management combines exposure data with threat intelligence, exploit prediction, and attack path analysis.
* Customer feedback cited positive reception for Charlotte AI™ and AgentWorks.
* ExPRT.AI provides exploit prediction, assessing likelihood of attacker use beyond CVSS scores.
* Falcon Exposure Management features include Attack Path Analysis, Asset runtime enumeration, and Compensating control awareness.
* An example showed a company reducing critical vulnerabilities in its DMZ by 98% using ExPRT.AI and asset criticality.
* Falcon Exposure Management unifies exposure management and security operations on the same platform.
* AI capabilities aim to reduce manual work between risk finding and action.

The narrative positions a successful market evolution from reactive vulnerability discovery to proactive, context-aware risk management. The central pattern involves shifting focus from *what is vulnerable* (vulnerability management) to *what is exposed and what is likely to be exploited* (exposure management). This suggests that the friction in traditional security operations lies not just in finding flaws, but in establishing the causal link between a flaw and its potential exploitation within an active environment.
The integration of AI—specifically through Charlotte AI and AgentWorks for workflow automation, and ExPRT.AI for exploit prediction—serves as the mechanism to bridge this gap by automating the complex process of contextual risk assessment. This creates a powerful leverage point: shifting analyst capacity from data aggregation (assembling evidence) to decision-making (acting on context). The success demonstrated in the Intermex case highlights that applying predictive modeling and environmental awareness directly translates into measurable remediation impact, validating the premise that understanding adversary tradecraft is more critical than simple severity scoring.
The implication for human agency is substantial: if AI can handle the heavy lifting of context gathering and prioritization, it frees up cognitive space for strategic response. The tension arises in determining where human judgment remains essential—in setting the ultimate remediation goals—versus where automation is effective—in correlating disparate data points. The potential pitfall is that relying too heavily on automated exposure management could lead to an over-reliance on algorithmic certainty, potentially obscuring the nuanced, non-quantifiable context that sophisticated adversaries exploit.
Bridge Questions: If AI automates prioritization, what specific human oversight mechanisms are necessary to ensure the derived risk scores account for novel or zero-day adversary techniques not yet reflected in existing threat intelligence? How should organizations structure roles to maximize the value of automation while preserving critical strategic judgment in the remediation cycle? What are the long-term risks associated with fully autonomous remediation workflows?