Agile Governance and Cognitive Automation in Cloud Security Operations
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Abstract
Rapid DevOps cycles and dynamic cloud platforms demand governance models that keep pace without stalling delivery. Concurrently, security-incident workloads overwhelm teams unless automated. We introduce LeadAutoSec, a unified approach combining an Agile Governance Layer that delegates low-risk security decisions to AI agents within sprint workflows, and Cognitive Automation that uses NLP to triage tickets and recommend fixes. In two controlled studies with a 10-sprint migration project and 1,000 ticket incident simulation, we observe:
• 58 % fewer manual policy exceptions and 25 % improved sprint-predictability under AI-delegation
• 72 % triage accuracy and 46 % reduction in mean time to resolve (MTTR) with NLP automation.
• Leadership favour “human-in-the-loop” for high-risk fixes (67 % approval) but accept full automation for low-impact changes.
We detail framework architecture with Mermaid diagrams, evaluation methodology, results, and discuss trade-offs, limitations, and future work.
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References
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