Has your automation earned the authority you’ve given it?
Automated controls can execute correctly and still make the outcome worse. Safety Automation tests what happens because the control acts — and whether the evidence justifies the authority it has been given.
What is Safety Automation?
Safety Automation is the practice of testing whether automated decisions and actions actually improve outcomes before they are trusted to act independently.
Automation is now making decisions across industries: restarting services, approving transactions, adjusting industrial processes, routing vehicles, changing capacity, detecting fraud, recommending treatment, and allowing AI agents to take action.
The action can execute exactly as designed and still make the situation worse.
The Assurance Gap
Most organisations can show that automation worked technically.
Far fewer can show that the decision itself was beneficial.
That gap matters whenever an automated control can affect customers, operations, money, safety, resilience or trust.
How Safety Automation Works
We test the effect of the automated action, not only whether it executed correctly.
Where possible, we compare the outcome with the control active against a credible alternative or counterfactual. We measure whether the action reduced harm, had no material effect, amplified harm, or remains unsupported by sufficient evidence.
The Principle
Automation should earn authority through evidence.
The greater the consequence of a machine acting, the stronger the evidence required before it is allowed to act without human intervention.
Certified Assurance Platform
RED HAT CERTIFIED · BUILT FOR OPENSHIFT
Safety Automation is not only a methodology. We built a certified implementation to run controlled assurance experiments inside enterprise OpenShift environments.
The platform creates repeatable conditions, exercises automated controls, captures the resulting evidence and supports counterfactual comparison between automation active and suppressed.
Create the condition
Inject latency, errors and controlled service disruption to reproduce the conditions under which automation has to act.
Exercise the control
Test restart, recovery, failover and related automated responses under controlled conditions.
Compare the outcome
Run the control active and suppressed to isolate what changed because the automation acted.
Capture the evidence
Collect experiment data, operational metrics and outcome measures into a reproducible evidence record.
Produce an assurance result
Use the evidence to determine whether the control reduced harm, was neutral, amplified harm or remains unproven.
The controls executed as designed. The outcomes got worse.
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Runs in your OpenShift namespace — no cluster-admin required
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Install it yourself — no SRE or operator needed
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Development & staging only — structurally can't reach production
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PDF/A-1b audit reports - ISO archival standard — regulator-ready evidence
We are a RedHat Certified Partner