為何法律AI需要加密證明，而不僅僅是免責聲明

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Why Legal AI Needs Cryptographic Proof, Not Just Disclaimers

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I learned to read adversarial systems in juvenile hall, jail, and Soledad Prison. I was in my early 20s, serving time for crimes that funded a heroin habit I developed when I was 15. The social dynamics inside — who’s lying, who’s concealing, who’s about to move against you — were a matter of survival. You learned to detect deception or you didn’t last.

I cut my hair and earned my GED while still in Soledad, and after I got out, I put myself through college and law school. Most people thought I was delusional. My own mother suggested I try to become a paralegal instead. But NYU Law took a chance on me, and after a year-long character review, the California Supreme Court admitted me to the bar.

I’ve now spent over 30 years in complex product-defect litigation — the kind where you’re sifting through millions of documents to find evidence that a defendant knew their product had a propensity to kill people. I’ve negotiated settlements worth more than $3B, obtained the first court-ordered vehicle recall in U.S. history, and recently forced two other recalls involving over a million vehicles. The pattern recognition I developed in prison turned out to transfer remarkably well to detecting corporate concealment.

Last year, I watched colleagues get sanctioned for AI hallucinations. Then I read the Stanford RegLab study showing that Lexis+ AI and Westlaw AI hallucinate 17–33% of the time on complex queries, and that general-purpose LLMs hallucinate 58–82% of the time. I realized the industry was building on sand.

So I built something different.

The Problem: You Can’t RAG Your Way Out of a Reliability Crisis

The legal AI hallucination problem is structural, not incidental. Stanford RegLab (March 2025) found leading legal AI tools hallucinate 17–33% on complex queries. A Stanford Law study found sanctions are imposed on plaintiff attorneys at nearly double the rate of defendant attorneys — presumably because under-resourced plaintiffs’ counsel are using consumer-grade AI. In Noland v. Land of the Free, 114 Cal. App. 5th 426 (2025), an attorney used ChatGPT, Claude, Gemini, and Grok for appellate briefs. Twenty-one of 23 case quotations were fabricated. The $10,000 in sanctions and the State Bar referral were not.

The Noland court’s conclusion: “AI hallucinates facts and law to an attorney, who takes them as real and repeats them to a court.”

The vendors’ answer is Retrieval Augmented Generation (RAG): ground the AI’s outputs in retrieved documents and the hallucinations go away. Except they don’t. The Stanford numbers are with RAG. These tools retrieve fragments, lose context, and hallucinate anyway. The architecture is fundamentally unsound, but the marketing says otherwise.

The current mitigation strategy is “human-in-the-loop” — lawyers checking every citation. This doesn’t scale. When you’re reviewing 500,000 documents in discovery, manually verifying every AI output negates the reason you’re using AI in the first place.

The Regulatory Catalyst

In June 2025, the U.S. Judicial Conference approved proposed Federal Rule of Evidence 707 for public comment. The rule requires AI-generated evidence to meet Daubert reliability standards, which expert testimony must also satisfy. Courts will need to evaluate whether training data is sufficiently representative, whether opponents have sufficient access for adversarial scrutiny, and whether the process has been validated in similar circumstances.

How do you prove what data went into a black-box API call? How do you demonstrate sound reasoning when you can’t inspect the inference environment? How do you validate consistent methodology with no audit trail?

You can’t. Unless you built for it from the ground up.

Public comment ends February 16, 2026. FRE 707 could be binding law as early as December 2026.

What We Built

Current e-discovery is like shooting queries into a pitch-black room. You hope you hit something. Lawyers and paralegals still “code” documents the way they did when they came in paper boxes — just on screens now. If your keyword search misses a synonym, you miss the smoking gun.

We built something different. With AI assistance (yes, the irony), I created Discovery Auditor Lite: a working prototype that ingests entire document productions and constructs a hybrid knowledge graph — mapping people, entities, events, relationships, communication threads, and temporal patterns across the full corpus. It currently holds 440,000+ documents (2 GB of content), runs entirely locally on a Mac Mini, and completes AI queries in seconds.

Discovery Auditor isn’t searching the data. It’s mapping it. When you ask “What did the engineers know about the defect?”, the system doesn’t grep for keywords. It traverses a graph that already understands who talked to whom, when, about what.

That alone would be valuable. But the real breakthrough is what comes next: the system detects what’s missing.

Using the same map, Discovery Auditor flags broken email threads (replies without parents), suspicious timing gaps around key events, missing attachments referenced in text, custodians who should appear but don’t, privilege log entries that are inconsistent or overly broad, redactions that don’t match stated justifications. In testing on real case data, the prototype flagged hundreds of potentially withheld documents through timing anomalies alone. This is the same methodology I used manually in a case against Toyota; it took me eight months to find what the software now finds in minutes.

No other platform does this. They can’t, because they don’t have the map.

The verification layer runs on top: every document is BLAKE3-hashed at ingestion, every text chunk gets its own hash, and an append-only ledger maintains a tamper-evident audit trail. The AI can only cite verified chunks. Ollama/Mistral runs entirely on-device. No data leaves the machine, and there are no cloud API calls to leak privileged material.

The Full Platform

The prototype proves the concept on a Mac Mini. The full platform, called Sigra (which means “to conquer” in Icelandic) adds hardware-level verification through Trusted Execution Environments (AMD SEV-SNP or Intel TDX).

We route bulk processing (OCR, indexing) through standard infrastructure. High-stakes reasoning runs inside TEEs with cryptographic attestation: signed audit trails for every inference showing what model version ran, what data was input, what the execution environment looked like. This creates evidence that’s self-authenticating under FRE 901/902. Even our own engineers cannot access client data processed within the secure enclaves.

The TEE architecture also solves a problem most legal AI vendors don’t talk about: lawyers can’t ethically upload privileged material to platforms they can’t verify. So they upload fragments that average around 20% of their case files. Our architecture guarantees that client data is mathematically unreadable by Sigra personnel, even during processing. So lawyers can upload everything without thinking twice, and that 5x data advantage compounds with every analysis. And our Cost Lock policy includes bring-your-own-storage or storage at cost (~2¢/GB versus the $25–30/GB that incumbents charge), so data sovereignty doesn’t come with a penalty.

There’s a collaboration angle, too: the same guarantees that let firms trust us with complete files let competing firms share intelligence without exposing work product to each other. But that’s a longer conversation.

The Team

I’m not building this alone. I first retained Mike Pecht as an expert witness 28 years ago in the case that resulted in the first court-ordered recall and the $2.7B settlement. He’s a world-renowned expert in electronics reliability and the George E. Dieter Chair Professor at the University of Maryland, where he founded and directs the Center for Advanced Life Cycle Engineering (CALCE). We’ve worked together on major defect cases ever since and just submitted a paper to IEEE Access on detecting strategic document withholding. Sigra’s methodology is the product of that collaboration.

Our CTO, Hasina Andriambelo, is the architect of Sigra’s verification infrastructure. He’s a Senior Technology Architect at Infosys in Paris and is a PhD candidate in Cybersecurity and AI from Edinburgh Napier, where his work on Binius zero-knowledge proofs achieved 90% reduction in proof size for privacy-preserving federated learning. His expertise in post-quantum cryptography and trusted execution environments is exactly what the full platform requires.

Why This Matters Beyond Legal

The verification architecture we’re building isn’t legal-specific. Any domain where AI outputs carry real-world consequences — medical diagnosis, financial analysis, engineering safety — faces the same fundamental problem: how do you prove the AI’s reasoning was sound?

“Trust me, the AI said so” isn’t going to cut it.

Happy to discuss the architecture, the legal landscape, or how this fits e-discovery workflows. If you want to poke holes in what we’ve built or test it on real data, I’m reachable at jlf@sigra.io.

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Written by Jeff Fazio

Jeffrey L. Fazio received a J.D. from NYU, is a partner with Fazio | Micheletti LLP, and the founder of Sigra, a verification-first complex-litigation platform.

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