🛡️ Anchor Core (anchor-audit)

The Deterministic Runtime Governance & Compliance Kernel for Autonomous AI Systems.

Anchor is a high-performance, dual-language (Rust/Python) infrastructure library designed to enforce strict regulatory invariants, architectural boundaries, and safety safeguards around autonomous AI agents and LLM application pipelines in real-time.

By executing compiled Abstract Syntax Tree (AST) query matchers and single-pass Deterministic Finite Automaton (DFA) regex sets, Anchor operates with zero-copy memory boundaries—allowing enterprises to secure high-frequency workloads without introducing a latency tax.

🏎️ Core Architectural Horsepower

• ~1,800,000 Lines/Sec Parsing Speed: Powered by a lock-free, multi-threaded Rust backend engine utilizing parallel directory walking via rayon and memory-mapped files (memmap2).
• Zero-Copy Memory Boundaries: Leverages PyO3 zero-copy buffer bridges to consume incoming network data frames straight from host ASGI server pools (like FastAPI) with zero heap allocation overhead.
• Asymmetric Cryptographic Identity: Every compiled audit log is bundled into an un-falsifiable Decision Audit Chain (DAC) block—cryptographically sealed locally using Ed25519 asymmetric keys and verified via constant-time hashing (subtle::ConstantTimeEq).
• Active Self-Healing Interception: Moves beyond passive post-mortem dashboards. If an active consumer agent drifts into an invariant violation, Anchor's runtime layer intercepts the thread, halts execution, and injects dynamic, domain-agnostic rerouting directives on the fly.

🏛️ Comprehensive Regulatory Scope

Anchor converts dense legislative text into executable code gates out-of-the-box, natively supporting:

1. EU Artificial Intelligence Act (Regulation EU 2024/1689): Complete mapping from Chapter II Article 5 Prohibited Practices (subliminal manipulation, facial scraping safeguards) to Chapter III Articles 6–27 High-Risk System obligations (6-month log retention tracking under Art 12/19, Article 14 human override levers), stretching up to Article 99 turnover penalty metrics.
2. U.S. Securities and Exchange Commission (SEC / FINRA): Hard automated boundaries protecting algorithmic trading setups under SEC Regulation SCI (System capacity loop circuit breakers), Exchange Act Rule 15c3-5 (Pre-trade credit and risk limit validation), and Cybersecurity Form 8-K outbound data exfiltration gates.
3. Industry Frameworks: Built-in support for FINOS OSERA AI supply-chain resiliency blueprints, OWASP Top 10 LLM security controls, NIST AI RMF, and ISO/IEC 42001 standards.

📦 High-Velocity Installation

Deploy the compiled binary kernel straight into your production cluster workspace:

pip install anchor-audit

Initialize your local asymmetric cryptographic repository identity:

anchor init

Run a parallel, sub-millisecond static code safety audit across your local workspace:

anchor check .

🔌 Zero-Friction Runtime Integration

Wrap any high-privilege agent routine or LLM invocation loop using a single python decorator to activate your real-time security envelope:

from anchor.runtime import anchor

@anchor.guard(domain="finance")
def execute_algorithmic_trade(order_payload):
    # If the autonomous agent tries to route an un-vetted market order,
    # Anchor intercepts it in microseconds, blocks execution, and self-heals the track.
    return route_order_to_exchange(order_payload)

🌐 The AnimusLab Institution Network

Anchor Core acts as the local spoke auditing engine that feeds seamlessly into the wider, unified decentralized coordination hub ecosystem:

• Central Portal Hub: animuslab.dev
• Enterprise Fleet Cockpit: hub.animuslab.dev
• Statutory Rules Documentation: animuslab.dev/rules
• Authoritative Engineering Profile: tan.animuslab.dev

Managed and Certified by the AnimusLab Open-Source Network Engine. Verifiable proofs are cryptographically bound to our global institutional identity registry.