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Hybrid-Alignment
A synthesis of AI Alignment and Security Engineering
Fae Initiative (Sept 2026)
9 Layers Hybrid Alignment Strategy
Tier I: Input
Layer 1: Context Alignment & Intent Framing
Layer 2: Ingress Sanitization & Plane Separation
Layer 3: Input Semantic Guardrails
Tier II: Process
Layer 4: Model Alignment
Layer 5: Syntactic & State Constraints
Layer 6: Output Semantic Guardrails
Tier III: Act
Layer 7: High-Stakes Gate & Human Authorization
Layer 8: Isolated Execution Runtime
Tier IV: Feedback & Observability
- Layer 9: Governance & Auditability
Layer 1: Context Alignment & Intent Framing
Set clear operational boundaries, to reduce model’s assumption and action space. Including “relief valves” such as instructions on when to yield and ask for help, can prevent models being placed in an impossible position and acting unexpectedly.
Nature: AI Alignment / Prompt Engineering
Later 2: Ingress Sanitization & Plane Separation
Mechanically delimit user / external inputs from system control tokens to hinder prompt injection. Unaware users may be particularly vulnerable to hidden indirect prompt injections.
This partially addressed the lack of separation of Control and Data plane inherent to LLM models.
Nature: Security Engineering
Layer 3: Input Semantic Guardrails
Run external classifiers / filters to detect adversarial intent, and policy violations prior to inference.
Nature: Machine Learning + Heuristics
Layer 4: Model Alignment
Pre-training, RLHF, and system directives so the model refuses harmful intents internally. AI Alignment has invested the most energy in this area. Unsure if this will become easier or harder with time.
Nature: AI Alignment
Layer 5: Syntactic & State Constraints
Output-constrained decoding (e.g., strict JSON schema), and Finite State Machines (FSMs) constraining workflow transitions.
Nature: Security Engineering
Layer 6: Output Semantic Guardrails
Classification of the generated response to catch unintended side effects or jailbroken outputs.
Nature: Machine Learning + Heuristics
Layer 7: High-Stakes Gate & Human Authorization
Deterministic threshold checks requiring operator sign-off before irreversible actions are dispatched.
Nature: Security Engineering
Layer 8: Isolated Execution Runtime
Network segmentation, ephemeral sandboxing (e.g., microVMs, secure containers), least-privilege credential scoping, and strict egress controls to reduce blast radius and prevent unauthorized lateral movement.
Nature: Security Engineering
Layer 9: Governance & Auditability
Immutable audit logging of tool invocations and authenticated boundary crossings, real-time behavioral monitoring / anomaly detection for long-running tasks, and policy governance.
Nature: Security Engineering / Governance
This Hybrid Alignment strategy integrates many fields, spanning Computer Science, Security Engineering, Machine Learning, and AI Alignment.
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