AI サービスが一時的に利用できないため、復旧後に翻訳を補完します。
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. About Tamnoon 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. Cloud security remediation challenges A security engineer can spend hours investigating a single finding. Often, it traces back to the same root cause as dozens of other alerts. Alert volume overwhelms headcount: CNAPPs and scanners generate thousands of findings. Teams can’t triage them all, so critical exposures sit unresolved while risk compounds for months. Investigation is manual and repetitive: Each finding requires context gathering on who owns the resource, what depends on it, whether the fix is safe. This eats hours of senior engineer time, repeated across every item in the backlog. Fear of production impact blocks automation: Teams hesitate to automate remediation because they can’t risk taking down production. Without confidence that a fix is safe, nothing moves. Reactive prioritization: Without a way to cluster related findings or score remediation confidence, teams work through alerts linearly rather than addressing root causes that resolve many issues at once. Tamnoon’s solution addresses the full pipeline from finding ingestion to validated fix. The platform treats these challenges as interconnected rather than isolated problems. Solution overview 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. Agentic architecture on Amazon Bedrock The platform delegates work to four specialized agents, each responsible for a distinct phase of the remediation pipeline: An investigation agent draws on training from millions of real customer cloud environments to determine context, filters that context through a large language model (LLM)-as-a-judge validation layer. It reasons through the remediation goal, operational scope, and cloud-provider-specific verification, producing read-only investigation steps and a complete investigation plan. A safety-scoring agent builds an evidence chain per asset, combining automated verification results with analyst findings. It classifies each finding’s remediation as SAFE, RISKY, or AWAITING DATA with a deliberately conservative bias. When in doubt, it downgrades. A remediation agent activates only for findings that the safety agent has deemed safe. It reasons through ordering, dependencies, and conditions, then produces a remediation plan in which every step follows a backup, remediate, and verify structure and includes rollback guidance. A code-generation agent turns plan steps into executable remediation, but only after an input guardrail screens for relevance and prompt injection. Every artifact passes an automated generate-and-review loop in which a separate reviewer agent must approve the output. Why Amazon Bedrock 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. How Tamnoon integrates with CNAPPs and cloud security tools 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. Figure 1: Tamnoon in action Tamnoon AWS architecture 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 How Tamnoon maintains zero production issues 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. That discipline is reinforced by constant self-critique. Each generation step is paired with a reflection step that checks the output and feeds its critique back into regeneration, so the system is always reviewing its own work rather than trusting it outright. Those recommendations are also kept grounded in reality. Agent output is validated against automation results, user feedback, and training data drawn from real fixed alerts, and when the model cannot reach an accurate result on its own, a human is brought into the loop. The final safeguard is at the moment of execution. The AI never holds cloud credentials. Instead, execution is brokered through short-lived, tenant-scoped credentials under customer-controlled trust, running in an isolated environment kept separate from the agents that plan the work. Wrapping all of this is human governance: remediations flow through an approval workflow with a full audit trail, and every AI capability is proven internally before it ever reaches a customer. Data security and compliance Tami reasons over sensitive customer data: alert payloads, resource identifiers, misconfigurations, and remediation context. That data must stay inside the AWS trust boundary, run with least privilege, and stay auditable. Standardizing on Amazon Bedrock makes that possible. Sensitive content stays inside AWS Every Tami workload invokes Anthropic Claude on Amazon Bedrock through the ChatBedrockConverse interface, rather than calling a third-party LLM endpoint. That single choice puts the full AI supply chain under one AWS native service, which has real consequences for how customer data is handled. Inferenc [truncated for AI cost control]