Executive summary
TrigGuard Sovereign is a high-assurance governance layer for organizations operating AI agents in regulated environments. It provides a Deterministic Authority Kernel that ensures AI agents operate strictly within human-defined boundaries, reducing audit preparation time and mapping directly to EU AI Act (High-Risk Systems) and GDPR.
The compliance bridge
TrigGuard Sovereign maps to stringent EU AI Act requirements:
| Requirement | Sovereign feature | Business impact |
|---|---|---|
| Traceability (Art. 12) | Immutable audit chains | Cryptographically signed logs of every AI decision. Audit-ready evidence for regulators. |
| Human oversight (Art. 14) | Multi-party authorization | Prevent autonomous runaway agents. High-value actions require human-in-the-loop keys. |
| Robustness (Art. 15) | Deterministic kernel | Eliminate stochastic hallucinations in authorization logic. Guaranteed execution boundaries. |
| Data privacy (GDPR) | Air-gapped / VPC deployment | Zero-data leakage. No telemetry or PII ever leaves your infrastructure. |
Core capabilities
- Sovereign infrastructure, Deployment within your private VPC or on-premises (air-gapped). TrigGuard never sees your data; you own the binary and the environment.
- Policy-as-code (GitOps), Governance managed via version-controlled configuration. Review, test, and audit authority logic like production code.
- HSM integration path: decision-signing keys isolated in a customer-managed HSM. FIPS 140-2 validated modules available on request at production launch.
- Kill-switch protocol, Immediate, system-wide revocation of AI agent authority across all clusters in <50ms.
Financial indemnity & SLA
- 99.999% availability, Mission-critical uptime for high-frequency environments.
- Regulatory indemnity, Technical documentation and architectural proofs for Article 13/19 filings.
- Support, 24/7/365 mission-critical response with 15-minute SLA.
Procurement context
Authority Access: Starting at $25,000 USD / year.
Deployment: Standardized Helm charts for Kubernetes or standalone binary for air-gapped hardware.
Security clearance: Architected for GovCloud and SOC2 Type II environments.
"TrigGuard Sovereign moves the conversation from 'Can we trust the AI?' to 'The AI is technically incapable of violating our policy.'"
The TrigGuard deterministic authority kernel
1. Inference / enforcement separation
TrigGuard operates on Inference/Enforcement Separation. The AI agent uses probabilistic models (LLMs) to reason; the TrigGuard kernel uses a deterministic finite-state machine to authorize. No neural weights in the gatekeeper.
2. Enforcement pipeline
Four-stage, single-pass pipeline (published targets: kernel <5ms · p99 hot path; end-to-end evaluation <15ms · p99; measured envelope: kernel p99 0.018 ms, sequential pipeline p99 1.28 ms, 10 VU concurrent p99 4.59 ms - see /benchmarks):
- Normalization, Heterogeneous agent outputs (JSON, action calls) → standardized Action Schema.
- Contextual hydration, Real-time state from protected "Source of Truth" databases; bypasses agent memory to prevent data poisoning.
- Policy evaluation (WASM), Policies compiled to WebAssembly. Sandboxed, memory-safe, computationally deterministic. Same inputs → identical authorization result.
- Cryptographic signing, Every GRANT signed with hardware-backed key. Downstream systems accept only valid TrigGuard Sovereign signatures.
3. Mathematical determinism
Zero-hallucination guarantee: The kernel contains no neural weights; it is 100% algorithmic. Core logic is written in Rust, enabling formal verification of memory safety and state transitions (DO-178C / ISO 26262 alignment).
4. Security hardening
- Anti-tamper execution, Kernel runs in a TEE (e.g. Intel SGX, AWS Nitro Enclaves). Root on the host cannot inspect or modify authorization logic at runtime.
- State-pinning, Immutable ledger of in-flight authorizations to prevent double-spend and race-condition attacks.
5. Integration specs
Protocol: gRPC / Mutual TLS (mTLS) 1.3.
Policy language: Starlark (deterministic subset of Python) or Rego (Open Policy Agent).
Throughput (measured / projected): ~4,200 evaluations per second per single Node evaluator instance against the Swift core (k6, 10-100 VU; see /benchmarks); horizontally scaled deployments target 50,000+ decisions per second per cluster node by running ~12 to 15 instances behind a load balancer.
By implementing this kernel, you create a standard model of authority across the enterprise, whether the agent is GPT, Claude, or a custom model, they all pass through the same deterministic gatekeeper before touching production.