SASI (Symbolic AI Safety Intelligence) is the independent governance firewall for high-stakes AI. It operates as a deterministic, sub-50ms pre-model intelligence layer that secures conversational AI in regulated industries where "probabilistic safety" is not enough.Â
1. The Architectural Airlock
SASI decouples safety from generation. By operating in front of the Large Language Model, it creates a stable and compliant safety floor that remains constant even as underlying models change.
Symbolic intelligence, not prompt tricks
SASI relies on deterministic symbolic operators and explicit policy logic rather than fragile prompt engineering or hidden heuristics.
Fail-closed by design
If the safety stack encounters ambiguity or internal error, SASI defaults to conservative safety behavior so technical instability never becomes a safety breach.
Model agnostic stability
Normalize safety behavior across OpenAI, Anthropic, Llama, and other providers. Run cost efficient models while preserving the safety profile expected of frontier systems.
2. The MDTSAS Engine: Multi-Dimensional Risk Momentum
SASI moves beyond one-off keyword filters to track the evolving trajectory of user distress and intent across an interaction.
Multi-axis risk evaluation
Conversations are evaluated across multiple symbolic dimensions including distress, crisis escalation, relational dynamics, and boundary pressure rather than a single red flag score.
Ambiguity aware logic
Conflicting signals such as calm language paired with high risk themes are resolved deterministically in favor of safety first decisions.
Stateful symbolic modeling
SASI tracks volatility and boundary erosion using symbolic state vectors rather than isolated turn by turn judgments.
3. Technical Performance and Reliability
SASI is engineered for enterprise and regulated workloads where latency, uptime, and auditability are non negotiable.
- Low latency safety evaluation suitable for real time conversational systems
- Horizontally scalable for high volume chat, call centers, and multi tenant platforms
- Structured safety metadata returned on every call for monitoring and analysis
4. Hard Safety and Regulatory Enforcement
In regulated modes, safety is enforced at the system level.
Hard Safety Layer
An immutable enforcement layer that guarantees PII redaction and crisis escalation even if the application layer is misconfigured.
Adversarial defenses
Detection for jailbreak attempts, prompt injection, obfuscated self harm language, and other evasion tactics.
Jurisdiction aware operation
Supports EU compliant operation by disabling emotion recognition dimensions while preserving crisis and self harm detection in alignment with high risk AI requirements.
5. Auditability and Forensic Proof
SASI provides deterministic, regulator readable evidence for every safety decision.
Structured audit artifact
Each analysis can produce a SASIEnvelope containing policy identity, cryptographic policy hash, input and context hashes, actions taken, events, and active safeguards.
Policy version pinning
Every decision is cryptographically tied to the exact safety configuration that produced it, enabling replayability and post incident verification.
Built in invariants
SASI validates its own outputs for internal consistency without overriding decisions, enabling machine checkable safety guarantees.
Forensic reconstruction
A black box style record captures which safeguards were active, which thresholds applied, and which conditions triggered intervention without storing user content.
6. Multilingual and Global Safety Infrastructure
SASI includes deterministic language detection to support global deployments.
- Local script based detection with no external services
- Optional language specific model routing
- No behavior change when no mapping is configured
7. Integration and ROI
Cost efficiency
Reduce dependence on expensive frontier models for safety enforcement, enabling significant inference cost savings.
Liability mitigation
Move from black box explanations to verifiable proof. Demonstrate exactly why a message was flagged using structured decision paths, events, and policy hashes.
Next Steps
- Stop relying on prompt engineering as your primary safety control.
- Contact us for an architecture review and safety posture assessment.
- Install SASI middleware into your existing software stack to make every AI interaction governed, logged, and defensible.
