Rapid7 AI FAQ
Clear answers about how AI helps customers detect, prioritize, investigate, and remediate risk — without compromising privacy, security, or human judgment.
What customers should know first
Rapid7 uses AI to help security teams move faster, see risk more clearly, and make better-informed decisions across detection, investigation, prioritization, and remediation workflows.
Our approach is simple: use AI where it improves security outcomes, protect customer data, keep humans in control, and govern AI through the same privacy, security, and compliance practices customers expect from Rapid7.
Rapid7 uses AI to turn high-volume security data into clearer insights, faster triage, better prioritization, and more actionable guidance.
AI features are built into Rapid7’s existing privacy, security, access-control, and subprocessor governance framework.
AI assists analysts and customers. High-impact security actions remain reviewable, governed by configuration and human judgment.
AI features are tracked through inventory, lifecycle review, testing, monitoring, and vendor/subprocessor controls.
Customer prompts, inputs, and outputs are not used by third-party LLM providers to train or improve their models.
AI is scoped to security workflows such as triage, prioritization, query assistance, summaries, and risk context.
AI across the Rapid7 portfolio
Rapid7 uses AI across the portfolio to help customers move faster, see risk more clearly, and strengthen security operations. The examples below highlight how AI supports key product areas, from detection and investigation to exposure management, remediation, and cloud security. Section 5 provides additional product-specific examples and context.
InsightIDR and MDR use AI to triage alerts, suggest dispositions, assist investigations, generate LEQL queries, summarize log entries, and support internal SOC workflows. Agentic AI can also take on defined parts of an investigation by gathering relevant context, connecting signals, and surfacing evidence to help analysts move investigations forward faster.
Customer value: AI helps analysts reach the right decision faster without removing human oversight.
InsightVM, Exposure Command, Surface Command, and Remediation Hub use AI for predictive CVSS/Active Risk scoring, asset classification, and AI-generated remediation context.
Customer value: AI fills context gaps and makes prioritization more actionable.
InsightCloudSec capabilities help identify AI/ML cloud resources and risks; Cloud Runtime Security can generate incident stories and response guidance; AppSec can test LLM-specific attack paths.
Customer value: AI supports both defending with AI and managing AI risk.
Rapid7 MDR analysts may use internal AI assistants and draft report generation to streamline investigation and reporting workflows.
Customer value: Internal AI is used to improve service quality and consistency, with analyst review before customer-facing reliance.
01 / Capabilities and models
What Rapid7 means by AI, where it appears, and how models are chosen.
02 / Data, privacy, and training
What data is used, what is not used, and how to explain the boundaries clearly.
03 / Safety, reliability, and security
How outputs are tested, constrained, monitored, and kept in context.
04 / Governance, compliance, and transparency
The operating model behind responsible AI across Rapid7.
05 / Product Specific AI Capabilities
Examples of AI-powered and AI-risk visibility capabilities across Rapid7 products, and how they support security teams.
Useful customer links