Tamnoon built Tami to remediate cloud security findings
Tamnoon described how Tami uses Amazon Bedrock to investigate security findings and assess proposed fixes. According to the company, the solution reduced critical cloud risks by more than 90 % in 90 days without issues in production operations.
Tamnoon built Tami on Amazon Bedrock to investigate and remediate security findings. The tool receives findings from CNAPP platforms for protecting cloud applications and other security tools, standardizes their format and adds context about the affected resources and their exposure to risk. It helps security specialists address misconfigurations and other issues in environments running on AWS, Azure, Google Cloud and other providers.
Specialized AI agents investigate findings, assess the safety of fixes and prepare to carry them out. Amazon Bedrock allows different models to be selected through a single API based on task complexity and cost. The event-driven architecture uses AWS Lambda to collect findings, analyze them and carry out fixes; Amazon EKS provides continuously running services for the user dashboard.
Tami assigns each finding it processes a Remediation Confidence Indicator (RCI) rating: SAFE, RISKY or AWAITING DATA. The rating is intended to help teams determine which fixes can be automated and which require human review first. According to Tamnoon, the solution reduced critical cloud risks by more than 90 % in 90 days without issues in production operations; this result is reported in a case study on the AWS blog.
Why it matters
The case study shows a concrete architecture for connecting the detection of security issues with their investigation and remediation. For teams with a growing backlog of unresolved findings, assessing the impact of a change before making it in production and being able to connect existing security tools via API are key.
Two audiences, two different impacts
What this means
For individuals
Along with each processed finding, the security specialist receives an RCI rating that helps them decide whether the fix can be automated or requires human review.
For a business
Tami builds on existing detection systems and connects the intake of findings, prioritization and remediation. For businesses, it therefore plays a role in both cloud risk remediation and the review of changes that may affect production operations.
Risks and complianceCheck the original
Event sources
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