Tamnoon uses Amazon Bedrock agents to automate cloud security remediation
In short: Tamnoon built Tami, an agentic orchestrator on Amazon Bedrock that investigates cloud security findings from CNAPPs and cloud providers, scores remediation safety, and generates fixes with guardrails. The company reports a greater than 90% reduction in critical cloud exposure within 90 days with zero production issues. The system uses four specialized agents (investigation, safety-scoring, remediation, code-generation) each calling different foundation models through Bedrock, with Bedrock Guardrails and Cross-Region inference providing safety and scale.
This summary was generated automatically by AI from AWS Machine Learning's publication. It is our own text, not a copy of the original — facts, figures and quotes belong to the source, linked above and below.
What changed?
- 1Four specialized AI agents handle investigation, safety-scoring, remediation planning, and code generation
- 2Agents each call the Bedrock model best suited for their task through a single API
- 3Bedrock Guardrails mask PII and block prompt injection before data reaches models
- 4Remediation only executes via short-lived, tenant-scoped credentials, never held directly by the AI
- 5Every generated code artifact goes through an automated reviewer-agent approval loop
- 6Reported result: over 90% reduction in critical cloud exposure in 90 days, zero production issues
Why it matters
It's a concrete example of multi-agent orchestration for high-stakes, irreversible actions (changing live cloud infrastructure), showing how guardrails, confidence scoring, and credential isolation can make autonomous remediation safe enough for production use.
What it means for AI agents and contact centers
The layered safety pattern — read-only investigation, conservative safety scoring, approval workflows, short-lived scoped credentials, and automated reviewer agents — is a useful reference architecture for any team building autonomous agents that take real-world actions (e.g., modifying CRM records, triggering workflows, or executing tool calls) rather than just generating text.
Sources
- AWS Machine LearningOfficialPrimary sourceOriginal article →„How Tamnoon built AI-powered cloud security remediation with Amazon Bedrock“14 Oct 2026, 16:53
- Published by source
- 14 Oct 2026, 16:53
- Found by our system
- 14 Oct 2026, 17:11
- Summary generated
- 14 Oct 2026, 17:14
This article was written by AI from the original source. Facts, numbers and prices come from the source; missing values are marked “Not specified”. Legal notice, copyright and privacy