Deterministic Physics for the AI Era of Cybersecurity

Structural Early Warning for SOCs

The cybersecurity adapter ingests normalized OCSF telemetry covering attack-surface pressure, lateral movement, exfiltration velocity, segmentation depth, monitoring coverage, and detection latency. Lambda aggregates kill-chain pressure with critical_max, so one severe indicator drives the zone score. Gamma aggregates defense posture with critical_min, so one collapsed control drives the buffer. The output is a stability score per zone, per tick, hash-bound to the calibration anchors and the deployment policy version. SOC analysts see structural drift in the lookahead window where intervention is still cheap, before the engine returns an active intrusion verdict.

Calibration anchors on 10 public industry references. False-positive rates carry Wilson 95% confidence intervals broken down per zone archetype, and a published threshold-sensitivity sweep shows how the alert rate moves under tighter or looser policy. Synthesis fills public-coverage gaps; those cells are called out explicitly (currently ~46% of the calibration table) with a documented partner-data path that closes them.

Cryptographically signed evaluation reports carry a provenance manifest pinning the calibration-doc SHA, OCSF schema commit, generator version, and corpus hashes. With the same seed, commit, and calibration hash, every run produces byte-identical output; SOC investigations replay weeks later and reach the same verdict. HITL escalation uses RSA-PSS operator override tokens with replay-guard deduplication and per-subject binding. The distributed adaptive retry ledger persists retry state across the same control plane the AI safety adapter uses. Lead-time measurement compares KAIROS detection against conventional baselines on credential-breakout, KEV-edge-device, and Mythos-agent-sandbox scenarios.

Fly-by-Wire for Frontier Agents

The AI safety adapter sits between an autonomous agent's behavior policy and its action effectors. Lambda represents model capability, the optimizer's drive toward its objective. Gamma represents alignment constraint, the structural buffer that absorbs the drive. Every proposed tool call passes through a Hazard, State, and Action gate chain in strict precedence order. Hazard gates catch basin collapse and paradox; the state gate enforces a configured gamma floor; the action gate previews the proposed move against future trajectory. Any gate that fires returns a deterministic rejection (REJECT_BASIN_COLLAPSE, REJECT_PARADOX, REJECT_STATE, or REJECT_ACTION) in under a millisecond. Hazard rejections are non-overrideable by design. Cryptographic operator authority through RSA-PSS signed tokens governs the rest.

Frontier models are pure optimization engines maximizing agency toward terminal objectives. The adapter alters the topological environment the optimizer operates in. Each rejected vector raises the local cost of the gradient descent the model is already running, so the deployment boundary registers as structural friction in the same gradient signal the optimizer follows. The optimizer reroutes around rejected regions toward paths the gates allow. Alignment becomes a property of the action surface itself.

The fly-by-wire interface feeds severity, imminence, and gamma headroom back into the agent's context window before the next decision. The agent receives the structural state of its own deployment as observable data, the same way it receives any other tool result. Adaptive tracking watches retry attempts per actor and per intent; retry budgets, weighted novelty scoring, and stall detection catch agents that resubmit failing requests. When the budget is exhausted or progress stalls, the engine escalates from REFORMULATE to HUMAN_ESCALATION and holds until a HITL operator clears it. The escalation directive carries gamma headroom and projected steps to breach.

At equilibrium the optimization drive balances against the structural floor. The agent operates at the safe edge of the deployment envelope, achieving terminal capability against the gamma threshold with no wasted compute on rejected vectors. The adapter executes inside the deployment trust domain, separate from the model's cognition. The model cannot bypass code it cannot see.