AI workloads
Autonomous Security for AI Workloads.
Protecting AI models, training pipelines, inference services, and the infrastructure supporting them requires more than traditional security visibility. CyberActive AI is designed to connect workload security intelligence with infrastructure context, impact-aware decisions, and governed response.
From Assets to Recovery.
Protecting valuable AI assets
Model weights, checkpoints, training data, and inference infrastructure concentrate significant value and attract capable attackers.
Understanding workload dependencies
Models, datasets, compute, networking, orchestration, and identity interact. Security context must follow those relationships.
Detecting emerging threats
Adaptive, behavior-oriented detection designed to surface activity that departs from established workload patterns.
Limiting propagation
Dependency-aware risk evaluation identifies what a threat can reach next and informs targeted containment.
Coordinating recovery
Governed recovery workflows designed to restore workloads to a validated, secure state.
Illustrative threat scenarios
How the Architecture Is Designed to Respond.
Model asset exfiltration
Unusual access to model storage by a service identity.
Correlate identity, storage, and network context; recommend credential suspension and egress restriction.
Training data tampering
Unexpected writes to a curated training dataset.
Map affected pipelines and checkpoints; recommend pausing dependent training jobs pending validation.
Privileged credential compromise
An administrator credential used from an anomalous context.
Evaluate blast radius across clusters and workloads; recommend step-up verification or session revocation.
GPU resource hijacking
Unauthorized jobs consuming GPU capacity.
Identify the scheduling path and owning identity; recommend job termination and access review.
Inference endpoint abuse
Abnormal request patterns against a production inference API.
Assess service impact; recommend rate limits or token revocation without taking the service offline.
Orchestration compromise
Suspicious changes in the Kubernetes control plane.
Trace affected namespaces and workloads; recommend isolating the change path and restoring known-good configuration.
Capabilities describe the platform as designed. Outcomes are intended benefits and are validated per engagement; integrations are confirmed during technical evaluation.
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.