A high-performance, Rust-based validation layer for AI agents. Ensuring every transaction is compliant, authorized, and cryptographically sound before it touches critical enterprise infrastructure or a blockchain.
Cryptographic verification of execution environments. Ensures your AI agents are running exactly the code you deployed, untampered.
Built with Tokio and Axum for extreme concurrency. Minimal memory footprint and sub-5ms response times for validation endpoints.
Hard limits, velocity checks, and anomaly detection prevent AI agents from executing runaway financial operations.
Architected with European regulatory frameworks in mind. Extensive structured logging guarantees auditability for every single intent.
ATL-Trust is architected to slot between your AI Agents and your execution layer (blockchain/exchange). It intercepts, validates, and signs intents in milliseconds.
ATL-Trust intercepts outgoing prompt context, deterministically redacts sensitive datasets (SSNs, emails, credit cards, names) at the local edge, and seamlessly re-identifies them when returning LLM responses.
ATL-Trust structures every validation event into a sequentially chained blockchain ledger. If an administrator, insider, or attacker attempts to alter a historical transaction or delete a log entry, the signature verification breaks instantly.
ATL-Trust operates on a strict zero-trust model. Our execution framework assumes every AI intent is fully compromised until cryptographic validation proves otherwise.
The European AI Act establishes strict rules for high-risk autonomous AI systems. Explore how to map Article 6 requirements directly to deterministic runtime guardrails.
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Article 14 mandates effective human oversight ('Human-in-the-Loop') for high-risk AI. Learn how to implement dynamic circuit breakers that pause execution for human approval.
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Deployers of high-risk AI under the EU AI Act must perform a Fundamental Rights Impact Assessment. Discover how to technicalize FRIA controls into edge validators.
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Article 5 of the EU AI Act bans subliminal manipulation, social scoring, and untargeted scraping. Learn how to build boundary isolation filters.
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High-risk AI systems must undergo conformity assessments before affixing the CE mark. We outline the technical validation artifacts required for certification.
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Compliance doesn't end at deployment. Article 72 requires continuous post-market monitoring. Learn how to build real-time compliance telemetry.
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Article 50 requires disclosing when users interact with AI or when synthetic content is generated. Explore gateway-level transparency injection.
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Providers of General Purpose AI models face specialized obligations under the EU AI Act. Learn how to build systemic risk controls around GPAI models.
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Regulatory sandboxes established by national authorities allow testing of innovative AI. Discover how to configure enclave-based sandbox environments.
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Fines under the EU AI Act reach up to €35M or 7% of global turnover. We analyze how deterministic guardrails prevent fineable compliance breaches.
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Retrieval-Augmented Generation (RAG) introduces severe vulnerability vectors when scraped documents contain hidden instructions. Explore defense strategies.
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Orchestrating multi-agent systems requires enforcing state transition rules. Learn how to build zero-trust state machines in Rust.
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Replay attacks can trick AI agents into executing identical database writes multiple times. Explore cryptographic nonce verification for tool calls.
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Red-teaming autonomous agents requires testing against jailbreaks, privilege escalation, and memory poisoning. We detail attack methodologies and defenses.
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Timing attacks and memory remnants can leak sensitive data from local LLM gateways. Discover constant-time execution and memory scrubbing techniques.
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Agents must prove they possess authorization credentials without exposing sensitive user identity keys. Explore ZK attribute proof implementations.
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Vector databases (Pinecone, Qdrant, Milvus) can be poisoned with adversarial embeddings. Discover semantic index hardening patterns.
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Long-lived keys stored on disk are vulnerable to host compromise. Learn how to generate ephemeral signing keys inside TEE enclaves.
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When an agent executes multi-step reasoning loops, subtle injections can compound recursively across iterations. Explore recursive loop screening.
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ISO/IEC 42001 is the international standard for AI Management Systems (AIMS). Discover how to map ISO controls to ATL-Trust guardrails.
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The Model Context Protocol (MCP) standardizes how models connect to tools and data. Learn how to secure MCP servers using proxy validation gateways.
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When an agent hallucinates or produces unparseable outputs, systems must fail safely. Explore deterministic fallback patterns.
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Financial settlement agents that execute micro-payments or trade executions require continuous auditing. We break down ledger settlement checks.
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The NIS2 directive and GDPR enforce strict data sovereignty rules. Discover how local edge nodes keep prompt data within sovereign borders.
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Looking ahead to 2030, autonomous AI agents will manage complex enterprise operations. We detail the roadmap for permanent human-agent safety.
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