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intelligence_src_029e2f07006b78ebcb
observed · 2026-10-12 05:29:01 UTC
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Fingerprint: e71aa6d523741f9f527f5071fd96527dbb781bc01e36c89fd9ec28cdb29f734f
intelligence_src_029e2f07006b78ebcb
observed · 2026-10-12 05:29:01 UTC
Written by: Jules Czarniak Introduction As highlighted in the Mandiant M-Trends 2026 report, the mean time-to-exploit (TTE) has dropped to -7 days, meaning vulnerabilities are often exploited a week before a patch even exists. To keep pace, many security teams are exploring how to integrate large language model (LLM) agents into their codebases, development environments and continuous integration and continuous delivery (CI/CD) pipelines for automated vulnerability discovery and remediation. However, deploying privileged artificial intelligence (AI) agents without mature integration processes introduces new architectural risks. In response to customer inquiries about how to safely integrate AI capabilities into vulnerability management workflows, this blog provides actionable guidance from Mandiant Consulting about how to establish operational guardrails for AI assisted vulnerability management, including several detailed scenarios. What each of these examples show is that security teams can accelerate workflows with AI while also upholding the structural integrity of their environments. We suggest that combining AI capabilities with deterministic controls and human intellig
Fingerprint: 73c1d7e0debe5d7488155f13b9b45d57ca199b1ba5a49fd193d68e4889ae47e1