Technology
An Infrastructure Decision Layer for AI Security.
Five architectural layers turn security and infrastructure signals into governed, coordinated action, with governance spanning every layer and no dependency on a single AI model provider.
The Architecture.
- L1
Infrastructure Signals
Security and operational signals from supported systems and intelligence sources.
GPU infrastructure · AI workloads · Identity · Networking · Storage · Cloud · Orchestration · Existing security tools - L2
Context & Dependencies
Asset relationships, infrastructure dependencies, behavior, and operational context.
Asset graph · Service relationships · Behavioral baselines - L3
Risk & Impact Intelligence
Threat correlation, potential propagation, operational exposure, and business-impact evaluation.
Correlation · Propagation paths · Exposure · Impact - L4
Decision Intelligence
Impact-aware decision-making, policy constraints, response planning, and approval logic.
Option ranking · Policy checks · Explainable rationale - L5
Response & Recovery
Coordinated containment, mitigation, recovery workflows, and restoration validation.
Containment · Mitigation · Restoration · Validation - Governance & audit across every layer
Model-agnostic architecture
Built for an Evolving AI Security Ecosystem.
CyberActive AI’s architecture is designed to combine infrastructure intelligence, proprietary decision technology, and security signals from multiple sources.
As frontier AI models continue to evolve, the platform strategy supports the potential integration of advanced AI capabilities without making the infrastructure decision layer dependent on a single model provider.
Signals in · any source
Existing security tools
SIEM, XDR and EDR, cloud security, and vulnerability management.
Infrastructure telemetry
GPU hosts and BMC, orchestration, identity, network, storage, and cloud.
AI security intelligence sources
Findings from advanced AI security models and research feeds, as they become available.
Planned · provider-independent
CyberActive AI Core
Proprietary decision technologyInfrastructure context
Asset, identity, and workload dependency model.
Risk & impact intelligence
Propagation paths, operational and business consequence.
Governed decision-making
Option ranking, policy checks, approvals, explainable rationale.
Governed action out
Infrastructure response controls
Identity, orchestration, network, BMC, and storage actions through customer-approved integrations.
Recovery workflows
Coordinated restoration, integrity validation, and outcome learning.
No third-party foundation-model integrations are claimed today. Any external AI capability would be added as an intelligence source only where implemented, validated, and authorized.
Governed autonomy
Autonomy Within Boundaries You Approve.
Operators decide how much authority the platform has, for which actions, on which infrastructure. Autonomous action is limited to action classes the customer has explicitly approved, and a human can override any step.
Observe
Analyze, correlate, and recommend. The platform makes no changes to production systems.
- Authority
- None. Recommendations only
- Human role
- Operators decide and act
- Typical use
- Evaluation, onboarding, highly sensitive systems
Workflow in this mode
- 01Detect and correlate signalsPlatform
- 02Evaluate risk and propagationPlatform
- 03Select candidate actionsPlatform
- 04Authorize actionOperator
- 05Execute, record, and verifyOperator
Approve
Prepare response actions for authorized human review. Nothing runs until a named approver accepts it.
- Authority
- Executes only after approval
- Human role
- Approve, modify, or reject each action
- Typical use
- Production and change-controlled infrastructure
Workflow in this mode
- 01Detect and correlate signalsPlatform
- 02Evaluate risk and propagationPlatform
- 03Select candidate actionsPlatform
- 04Authorize actionApprover
- 05Execute, record, and verifyPlatform
Autonomous
Execute pre-authorized action classes within customer-approved boundaries, then notify. Anything outside those boundaries escalates to a human.
- Authority
- Only action classes the customer pre-approves
- Human role
- Sets boundaries, is notified, can override
- Typical use
- Time-critical, well-understood, reversible actions
Workflow in this mode
- 01Detect and correlate signalsPlatform
- 02Evaluate risk and propagationPlatform
- 03Select candidate actionsPlatform
- 04Authorize actionPolicy
- 05Execute, record, and verifyPlatform
Defined authorization boundaries
Customers set which action classes, assets, and environments the platform may act on, and in which mode.
Policy enforcement
Every proposed action is checked against policy before it is offered or executed.
Human approval requirements
Approval rules define who approves which actions, including two-person rules for disruptive ones.
Action traceability
Each recommendation and action is recorded with its evidence, rationale, approver, and outcome.
Operational safeguards
Blast-radius limits, protected assets, and rate limits keep responses proportionate.
Human override
Authorized operators can pause, reverse, or override any action at any stage.
Recovery verification
Restoration is checked against a known-good state before systems return to service.
Customer-approved boundaries
The platform never receives unrestricted access to customer infrastructure. It acts only through integrations and permissions the customer grants, within boundaries the customer approves.
Positioning
More Than Alerts. More Than Automation.
Security platforms and advanced AI models provide essential visibility, analysis, and response. CyberActive AI is designed to add an infrastructure-aware decision layer that complements them.
| Category | Primary focus | How CyberActive AI complements it |
|---|---|---|
| SIEM | Log aggregation, search, and detection rules | Adds infrastructure dependency and impact context to correlated events |
| XDR | Endpoint, network, and cloud detection and response | Relates detections to AI workloads, GPU clusters, and operational consequence |
| SOAR | Playbook-driven response automation | Selects and governs actions by risk and impact rather than fixed playbooks |
| Frontier AI security models | Vulnerability discovery, threat analysis, and security reasoning | A potential intelligence source; CyberActive AI adds infrastructure context, impact-aware decisions, governed response, and recovery |
| Cloud security | Posture, configuration, and workload protection in cloud | Extends context across hybrid GPU and on-premises infrastructure |
| AI workload security | Model, data, and AI application protection | Connects AI assets to the compute, identity, and network they depend on |
| Infrastructure monitoring | Health, performance, and availability | Joins operational state with security signals for impact-aware decisions |
Capability status
Precise About What Exists Today.
- Available · implemented and demonstrable
- In Validation · developed, undergoing testing
- Planned · roadmap
- Verified IP · patent status confirmed
- Illustrative · conceptual or simulated
- Patented technology foundationVerified IP
- Core platform architectureAvailable
- AI model training and validationIn Validation
- Decision intelligence and governance modesIn Validation
- GPU infrastructure integrationsPlanned
- External AI intelligence-source integrationsPlanned
- Autonomous recovery orchestrationPlanned
- Interactive demonstrations on this siteIllustrative
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.