Critical cloud exposures pile up faster than security teams can close them. Cloud-native application protection platforms (CNAPP), cloud providers, and posture scanners generate more findings than any team can triage, and risk accumulates in the gap between detected and fixed. Most findings sit untouched, not because they’re hard to fix. Someone must investigate context, identify the owner, confirm the fix won’t break production, and then do it at a volume that outpaces headcount.
In this post, we share how Tamnoon built Tami, an agentic cloud remediation orchestrator on Amazon Bedrock, to close that gap. Tami autonomously investigates findings ingested from CNAPP and cloud-native sources and assesses whether remediation is safe. It then generates and orchestrates the fix with guardrails and governance wherever a change touches a customer’s cloud. The approach drove an over 90 percent reduction in critical cloud exposure in 90 days with zero production issues.
Tamnoon is an AI-powered cloud security remediation platform that works alongside CNAPPs. Its AI assistant, Tami, helps human cloud security experts investigate, prioritize, and safely remediate misconfigurations and issues across customer environments in AWS, Azure, Google Cloud, and other cloud providers.
Tamnoon integrates with the CNAPPs and cloud-native tools teams already use and turns their findings into validated, production-safe fixes. Most of the work including investigation, prioritization, and remediation planning happens before anything touches a live environment, which is what makes automated remediation trustworthy at scale.
A security engineer can spend hours investigating a single finding. Often, it traces back to the same root cause as dozens of other alerts.
Source link
