AI-Powered Deception
Policy-driven adaptive deception for realism, control, and measurable outcomes.
Adaptive deception applies configurable policy to supported honeypot services so interactions stay believable while remaining operationally controlled. Teams can run in observe mode for telemetry-first validation or enforce mode for live behavior shaping, then measure impact with effectiveness and strategy analytics.
Designed For Adaptive Deception Programs
SOC teams that need stronger signal quality from deception
Detection engineers tuning workflows with behavior-based evidence
Threat hunters validating adversary progression and intent
Security leaders who require controlled rollout and measurable results
What This Solves
Static decoys lose attacker engagement too early
Policy-driven adaptive behavior helps supported services remain plausible longer and capture richer adversary actions.
Teams need control over how deception adapts
Global and per-service settings let teams define when adaptive logic is disabled, observed, or actively enforced.
It is hard to prove adaptive settings are working
Built-in effectiveness and strategy metrics help teams verify impact instead of relying on assumptions.
Security Outcomes
Controlled realism at protocol level
Increase believability in supported services without giving up policy control and operational consistency.
Cleaner analyst signal
Use adaptive policies plus noise suppression controls to preserve high-signal interaction evidence.
Evidence-driven tuning
Adjust strategy based on measured metrics such as dwell behavior, strategy distribution, and repeat activity.
AI-Powered Capabilities
Adaptive mode control
Run off, observe, or enforce modes based on readiness and risk tolerance.
Per-service adaptive policy
Override adaptive settings by protocol or service to target where realism matters most.
Control and consistency guardrails
Use coherence controls, stage tracking, and response shaping options to keep behavior stable and intentional.
Adaptive telemetry enrichment
Capture adaptive decision context and strategy outcomes for analyst triage and detection tuning.
Noise baseline controls
Suppress repetitive background patterns while preserving high-signal interactions.
Measurable effectiveness analytics
Track score and distribution metrics to confirm adaptive behavior is improving outcomes.
How It Works (High-Level)
1. Define adaptive policy
Set global controls for mode, delays, response shaping, and noise handling for each honeypot.
2. Apply service-level overrides
Enable stricter or lighter adaptive behavior for specific protocols based on exposure and use case.
3. Observe or enforce in production
Start in observe mode to validate behavior, then move to enforce when controls are verified.
4. Tune from analytics
Use effectiveness, strategy distribution, and noise baseline insights to refine adaptive settings.
How Teams Use This
SOC quality sprint
Run observe mode on internet-facing services for two weeks, then move selected services to enforce mode once analysts validate improved signal quality.
Detection tuning cycle
Review adaptive telemetry outcomes weekly, adjust per-service controls, and update correlation logic for better triage precision.
Controlled expansion
Start with high-risk service groups, baseline effectiveness metrics, then extend policy-driven adaptation to additional zones with change control.
Operationalize Adaptive Deception With Control
Deploy adaptive policy where it matters most, measure impact, and continuously improve deception outcomes.
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