Decision intelligence
The Intelligence Behind Every Security Decision.
Finding a threat is only the beginning. CyberActive AI’s decision architecture is designed to evaluate what a threat affects, how it could spread, what each response would cost, and what policy allows, before any action is taken.
Nine factors
What Every Decision Weighs.
Advanced AI can identify and analyze a threat. Deciding how to respond in a live AI environment also depends on infrastructure, consequence, and authority.
Threat severity
How confident and how serious the detection is.
Affected infrastructure
Which assets are touched or reachable.
Workload dependencies
Which workloads rely on those assets.
Potential propagation
Where the threat can move next, and how likely that is.
Operational consequences
What a response would interrupt or degrade.
Business & financial impact
Value at risk, service commitments, and recovery cost.
Available response options
Actions the integrated controls can actually perform.
Governance policies
What is pre-authorized, what needs approval, and by whom.
Recovery considerations
What it takes to return to a trusted state.
Interactive decision scenario
Same Threat. Four Responses. Different Consequences.
A compromised privileged identity reaches the GPU orchestration control plane. Compare how each response would affect infrastructure availability, and how the governance mode changes who must authorize it.
Response options in this scenario
- Suspend identity & revoke sessionsLow residual riskNo service or training impact expected.
- Isolate training pool AMedium residual riskTraining interrupted; ~1h 40m of compute progress lost.
- Freeze the cluster control planeLow residual riskInference cannot autoscale at peak; training cannot recover from node faults.
- Monitor onlyHigh residual riskPotential loss of model weights; exposure continues.
Illustrative decision scenario, not a measured production result. Enable JavaScript to compare options interactively.
How a decision is formed
From Signal to Explainable Recommendation.
Each recommendation follows the same path, and each step leaves a record.
Correlate
Detections from many sources are joined into one situation using identity, asset, and timing context.
Map
The dependency model resolves what the threat can reach and through which privileges.
Evaluate
Propagation, operational disruption, and business impact are weighed for each candidate action.
Check policy
Options are filtered by governance policy and the customer’s authorization boundaries.
Explain
Each recommendation carries its evidence, rationale, expected impact, and required approvals.
Design principles
Decisions You Can Inspect.
Decision intelligence is designed to support operators, not to replace their judgment.
- Recommendations are explainable, not opaque scores
- Policy constraints apply before options are shown
- Disruptive actions always require human approval
- Business context comes from the operator
- Every decision is recorded with its outcome
- Outcomes inform future evaluations
The decision scenario on this page is an illustrative simulation in a hypothetical environment. It does not represent measured production results.
Your AI Infrastructure Is Too Valuable for Reactive Security.
See how CyberActive AI is designed to carry a threat from detection through infrastructure context, impact-aware decisions, governed response, and verified recovery, across AI workloads and GPU environments.