How Tamnoon built AI-powered cloud security remediation with Amazon Bedrock

來源原標題

How Tamnoon built AI-powered cloud security remediation with Amazon Bedrock

作者

Ishara Premadasa

原始標籤

Amazon Bedrock · Customer Solutions

原始連結

https://aws.amazon.com/blogs/machine-learning/how-tamnoon-built-ai-powered-cloud-security-remediation-with-amazon-bedrock/

摘要原文

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. Tamnoon’s solution addresses the full pipeline from finding ingestion to validated fix. The platform treats these challenges as interconnected rather than isolated problems. Tamnoon takes you from initial finding to executing the fix. The platform ingests findings from CNAPP, Cloud Security Posture Management (CSPM) providers, and other security tools and normalizes them into a unified schema. It then enriches them with asset and exposure context and prioritizes them. From there, the agentic layer takes over using a set of specialized AI agents, each running in an event-driven, serverless architecture on AWS, so every stage scales independently and fails safely. The platform delegates work to four specialized agents, each responsible for a distinct phase of the remediation pipeline: When the Tamnoon team set out to build Tami, the choice of foundation model (FM) platform was going to shape everything that followed. Tamnoon’s remediation agents don’t all do the same kind of work. Some perform deep investigative reasoning to understand the root cause of a security finding. Others handle high-volume validation, checking proposed fixes quickly and cheaply. Still others review generated code, balancing quality against cost on every task. Matching each of these jobs to the right model, without stitching together a different integration for each one, was a core requirement. That requirement is what made Amazon Bedrock the natural fit. Through a single API, each agent can call the foundation model best suited to its task. The most capable models handle the hardest reasoning. More efficient models take on the repetitive, high-throughput work. Review-focused models optimize the trade-off between output quality and cost. Because it’s one integration rather than many, models can be swapped or upgraded as the landscape evolves, without re-plumbing the architecture each time. Operational overhead at scale was another factor in the decision. As request volume grew, Tamnoon wanted to avoid building and maintaining its own logic to distribute traffic across regional capacity. Amazon Bedrock Cross-Region inference handles this automatically. It automatically routes inference requests to the optimal AWS Region within a defined geographic boundary, maintaining throughput without requiring any custom routing code to operate. Security was the final consideration, and for a cloud security company it was non-negotiable. With Bedrock, inference runs inside Tamnoon’s own AWS environment under AWS Identity and Access Management (IAM)-scoped permissions. Sensitive customer data never leaves the trust boundary. That gave the team confidence that the platform underpinning Tami met the same standard Tamnoon holds its own service to. Tamnoon sits downstream of the detection tools security teams already run. CNAPPs like Wiz, CrowdStrike, Palo Alto Cortex, Upwind, SentinelOne, Orca, and Cyera along with cloud-native services like AWS Security Hub and Amazon GuardDuty are the source of truth for what’s wrong in the environment. Tamnoon connects through API-based integrations and normalizes findings into a common format, so a misconfiguration looks the same whether it came from Wiz or GuardDuty. The detection tools remain the foundation. Tamnoon delivers the end-to-end remediation that follows. For every finding it works, Tami assigns a Remediation Confidence Indicator (RCI) of SAFE, RISKY, or AWAITING DATA. That rating tells teams what can be automated and what needs human review first. Tamnoon runs entirely within its own AWS accounts, isolated per environment. Users access the platform through a single secure entry point built on Amazon API Gateway, with encryption managed by AWS Certificate Manager and sign-in handled by Amazon Cognito. Everything behind that entry point runs in private networks across multiple Availability Zones, so the core platform is never directly exposed to the internet. Inside that environment, Tamnoon combines two kinds of compute. Amazon Elastic Kubernetes Service (Amazon EKS) runs the always-on services behind the dashboard, while AWS Lambda powers the event-driven work of collecting findings, analyzing them, and executing remediations. A set of AWS messaging and workflow services, including Amazon EventBridge, Amazon Simple Queue Service (Amazon SQS), and AWS Step Functions, coordinate these steps so large volumes of findings are processed reliably and in parallel, with no single bad record stalling the rest. The AI layer runs on Amazon Bedrock. Amazon Bedrock AgentCore , a platform to build, connect, and optimize agents at scale with any framework or model, gives Tamnoon’s agents a governed way to take action instead of open-ended access. Amazon Bedrock Guardrails then review both the request and the generated code before anything moves forward. Supporting services handle the rest of the platform: managed databases and caching for state, Amazon Simple Storage Service (Amazon S3) for playbooks and artifacts, and AWS Secrets Manager for credentials, so sensitive keys never live in application code. On the customer side, everything to the left of the AWS Cloud boundary is what customers connect into Tamnoon. They can link their own AWS, Azure, and Google Cloud accounts, plug in the security tools their findings come from, and connect the destinations where they want outcomes delivered, such as Slack, Jira, ServiceNow, and GitHub. Figure 2: Tamnoon AWS architecture Zero production issues is a reality for Tamnoon’s customers, and it is no accident. Autonomy at Tamnoon sits inside a defense-in-depth safety architecture, where each layer assumes the one before it might miss something. It starts before any fix is even drafted. No remediation is written until an independent safety assessment, focused on scope and deliberately biased toward caution, concludes that the change is safe to make. From there, the platform draws a hard line between reading and writing. Investigation steps are validated as strictly read-only, so the work of understanding a problem can never alter it. Only the remediation path is allowed to change anything, and every change it makes carries its own backup, verification, and rollback steps.