What is ISCIL?
The Inter-System Coherence & Integrity Layer (ISCIL) is a containment architecture that detects and dampens environment-level drift in AI-integrated enterprise systems. Introduced by Myriam Ayada (2026).
ISCIL operates at boundaries between systems. It does not inspect individual outputs, gate execution paths, or attempt to eliminate AI outputs' semantic openness.
Design philosophy. A healthy immune system does not achieve sterility but maintains homeostasis. ISCIL's success metric is not absence of ABOs but the environment's ability to absorb small deviations without amplification.
This reframes AI risk from reactive fault handling to proactive coherence preservation.
How ISCIL Works
- Boundary Telemetry — Monitors aggregate behavioural statistics at corridor boundaries: approval rates, category proportions, escalation rates, feedback magnitudes. No individual output is inspected.
- Coherence-Risk Scoring — Uses rate-of-change z-scores relative to a rolling baseline. Detects acceleration above normal variability. Seasonal or market-driven level shifts do not trigger alerts.
- Corridor-Level Containment — Discretisation guardrails: blind scalar offset before boundaries. Feedback damping: asymmetric attenuation, corrective signals boosted, amplifying signals dampened.
- Proportional Response — Containment scales with severity. Applied only within detected CRC. Blast radius expanded only if risk persists. Relaxes autonomously once stability returns.
Design Constraints
- Interface-level: No internal model inspection. Works with any system producing boundary outputs.
- Non-intrusive: No synchronous gating except during active containment.
- Graph-native: Acts on ISE corridor structure.
Simulation Results
In a simulation replicating a realistic AI-integrated underwriting pipeline, with four interconnected systems processing loan applications over 1,200 decision cycles:
- 39 excess defaults eliminated — 100% recovery.
- 78 timesteps active intervention — 6.5% overhead.
- ~t=50 detection point — ~40 ts faster than outcome monitoring.
- Direction-agnostic — detected both onset and cessation.
Beyond Brittle Semantic Contracts
To prevent context clash and downstream errors, many MLOps teams attempt to write rigid "semantic contracts" or force strict JSON schemas. This approach attempts to achieve sterility by breaking the pipeline whenever ambiguity occurs.
ISCIL offers an alternative: instead of brittle semantic contracts that destroy the utility of Generative AI, ISCIL acts as a continuous immunity layer, dampening drift dynamically without requiring absolute determinism.
What ISCIL Complements
| Approach | What It Does | ISCIL Relationship |
|---|
| MLOps monitoring | Watches model performance | ISCIL watches between models |
| Spec-closure | Constrains output format | ISCIL covers where spec-closure isn't feasible |
| Hardened boundaries | Reduces jump susceptibility | Doesn't address feedback persistence |
| Human review | Semantic interpretation | ISCIL enables selective, not blanket, review |