Zero production incidents should be the benchmark for any company looking at automated cloud remediation.
That also happens to be the track record Tami has across every cloud remediation it has executed to date. For security teams holding off on automation, it answers the only question that matters: will this break production?
What makes that track record possible? Three things:
- A read-only investigation of every finding
- Blast-radius analysis before any change
- A confidence rating that distinguishes what is safe to automate from what needs human review.
Most automated remediation tools cannot actually deliver that combination at scale across security tooling.
In conversations with over 800 cloud security teams, the single most common reason CNAPP alerts stay open is not that teams don’t know about them. It’s that they’re afraid to fix them. The blocker is one specific question: will this break production?
A misconfigured network policy can sever application traffic, an IAM role removal can interrupt a running service, and a database encryption change can require infrastructure recreation. Every fix carries the possibility of an outage, and the closer the resource sits to a paying customer, the higher the stakes.
That is why “automated remediation,” as a category, is treated with skepticism. The default assumption is that automation means speed at the expense of safety. For most platforms on the market, that assumption is correct.
See how investigation, blast radius reasoning, and confidence scoring combine to make production-safe automation possible.
What Makes Automated Remediation Unsafe
Three failure modes show up across every tool that has tried this and stumbled.
1. The system acts on alerts without context
A CNAPP flags an S3 bucket missing HTTPS enforcement. The automation tightens the policy. The bucket serves a legacy mobile client over HTTP that still has 50,000 active users. By the time anyone notices, support tickets are already flooding the queue.
The fix was technically correct. The action was unsafe because it was applied without first checking what depended on the existing configuration.
2. The system tests fixes in production
Some platforms validate remediation by running it. If the change breaks something, they roll it back. This sounds clever until your rollback leaves a 90-second window where the application is down, your CDN has cached the broken response, and your error budget for the quarter is gone.
Production is not a test environment. Anything that treats it like one will eventually cause an incident.
3. The system can’t tell SAFE apart from RISKY
Static severity ratings (”this CVE is critical, fix it”) don’t account for whether the resource is in production, whether it has dependencies, or whether the patch has known compatibility issues with the deployed runtime. Without that context, every alert gets the same treatment, and every fix carries the same blind risk.
What Makes Automated Remediation Safe
Production-safe remediation reverses each of those failure modes. Three principles, applied in sequence.
- Investigate before acting: Before any change is recommended, the system queries cloud APIs, traffic logs, configuration state, and dependency maps to build a complete picture of what fixing the issue will actually affect. None of this investigation touches production. It is all read-only.
- Reason about blast radius: Once the picture is complete, the system reasons about what could break. How many services depend on this resource? Is it in the request path of a production workload? Is the patch validated against the deployed version? The answers to those questions decide what the recommended action looks like.
- Score every fix before execution. Every alert receives a confidence rating: SAFE, RISKY, or AWAITING DATA. SAFE findings can proceed automatically. RISKY findings require human approval. AWAITING DATA findings are routed to the right team with the full investigation context. No fix reaches production without first earning a confidence rating that justifies the action.
That sequence is the architecture behind Tami’s Remediation Confidence Indicator, and it’s what the rest of this post is about.
How the Remediation Confidence Indicator Works in Practice
The clearest way to see it is to watch the same alert produce three different outcomes.
Imagine a CNAPP flags three S3 buckets for the same policy violation: no HTTPS enforcement. Same severity, same finding, same recommended fix. After Tami investigates, the picture changes.
- Bucket 1 is empty and not attached to any workload. SAFE. Tighten the policy automatically.
- Bucket 2 shows 100% HTTPS traffic in CloudTrail logs over the last 90 days, with no HTTP calls detected. SAFE. Enforce the policy automatically.
- Bucket 3 has 45 active HTTP GetObject calls in the last 90 days, traced to a legacy integration with an upstream partner. Enforcing HTTPS would break the connection. AWAITING DATA. Route to the application team with a full evidence trail and a remediation plan that coordinates the change with the partner.
Same finding. Three different outcomes. That distinction does not exist in remediation tools that treat alerts as undifferentiated work items, and it is the difference between a fix that closes a ticket and a fix that closes a ticket without breaking your application.
Why Production is Only Touched Once
There is a structural reason Tami has zero production incidents to date.
Across the full remediation workflow, only one stage actually changes anything in your environment: execution. Investigation, enrichment, blast radius analysis, and RCI assignment all happen against metadata, logs, and APIs. They never modify a resource, never create a test load, never simulate failure against your live workloads.
By the time Tami executes a fix, four things have already happened:
- The finding has been investigated against live cloud data.
- Dependencies and traffic patterns have been mapped.
- The RCI has been calculated and validated.
- The remediation script has been generated from a battle-tested playbook.
Production is touched once, with full evidence, after a confidence score justifies the action. Anything that cannot earn that justification doesn’t get auto-remediated. It gets routed to a human with the context they need to handle it.
What Teams Actually Get
Tamnoon publishes one number that summarizes the result of this approach: zero production incidents across the entire deployment history of the platform.
The other numbers follow from that one:
- Up to 95% reduction in MTTR versus manual remediation workflows.
- Up to 82% reduction in open cloud exposures within 90 days of deployment.
- 130,000+ alerts closed monthly across the customer base.
- A 25x increase in alerts investigated per analyst.
The headline isn’t speed. The headline is that speed is finally possible without trading away production stability. That is the unlock.
Achieve Speed Without Sacrificing Production Stability
Automated cloud remediation is safe when the platform can prove the fix won’t break production, score every action with a defensible confidence rating, and keep production out of the investigation loop entirely. Tami was built to deliver exactly that. Tamnoon’s track record of zero production incidents is the proof.
If your team has been holding off on automation because the safety story didn’t add up, book a 30-minute demo. Bring your top 10 unresolved CNAPP alerts. We’ll show you which ones are safe to fix today, which ones aren’t, and exactly why.
