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DESCRIPTION:   'Title: When Machines Attack Machines: Detecting AI-Autonomo
 us Cloud\n   Compromise at NHI Scale\n   Tags: Cloud Village | Creator Tal
 k/Panel\n   When: Sunday\, Aug 9\, 10:40 - 11:20 PDT\n   Where: LVCCW Leve
 l 3 W313 (Cloud Village Talks) - [1]Map\n\n   Description:\n\n   Two force
 s are converging to break cloud detection as we know it.\n   First\, non-h
 uman identities (NHIs) such as service accounts\, API keys\,\n   OAuth tok
 ens\, AI agent credentials now outnumber human identities by\n   ratios ex
 ceeding 100:1 in enterprise environments. 97% of these NHIs\n   carry exce
 ssive privileges\, 71% are never rotated within recommended\n   timeframes
 \, and over 40% are orphaned with no human owner. Second\,\n   attackers h
 ave begun using AI agents to compromise these identities at\n   machine sp
 eed\, performing hundreds of reconnaissance actions per\n   minute\, auton
 omously mapping privilege escalation paths\, and executing\n   lateral mov
 ement faster than any human analyst can respond. The result\n   is a detec
 tion problem that traditional security tools were never\n   designed to ha
 ndle: machine-speed attacks against machine identities\,\n   where the att
 acker's AI agent looks indistinguishable from the\n   defender's AI agent 
 in the logs. This talk presents the first\n   detection engineering framew
 ork purpose-built for this collision\n   covering how to baseline NHI beha
 vior\, detect AI-autonomous compromise\n   patterns\, and build response a
 utomation that can match attacker speed.\n\n   SpeakerBio:  Gowthamaraj Ra
 jendran\n\n   Gowthamaraj Rajendran is a Detection and Response Engineer a
 t Meta\n   with over 6 years of experience in cybersecurity. He specialize
 s in\n   building high-fidelity rules and improving detection engineering\
 n   practices at scale.\n\n   '\n\n   1. #LVCCW_Level3_South\n\n\n
DTEND:20260809T182000Z
DTSTART:20260809T174000Z
LOCATION:Cloud Village - LVCCW Level 3 W313 (Cloud Village Talks)
SUMMARY:When Machines Attack Machines: Detecting AI-Autonomous Cloud Compro
 mise at NHI Scale
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