Daniel Garrie
JAMS Neutral | Founder, Law & Forensics | Faculty Harvard
New York City, New York, United States
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Daniel B. Garrie is the Founder and Managing Partner of Law & Forensics LLC, a specialized legal engineering firm focused on cybersecurity, digital forensics, and e-discovery. He is also a Neutral (arbitrator, mediator, and Special Master) with JAMS, and Adjunct Faculty at Harvard University, where he teaches courses on computer forensics, cybersecurity law, cryptocurrency, and blockchain.
Daniel holds a J.D. from Rutgers School of Law and an M.A. in Computer Science (Honors) from Brandeis University. This dual fluency in law and technology has defined his career. He co-created the JAMS Artificial Intelligence Dispute Resolution Rules—the first arbitration framework purpose-built for AI conflicts—and holds four U.S. patents in cybersecurity forensic technology. He has served as a court-appointed expert and special master in hundreds of federal and state proceedings, including serving as Special Master for In Re: Facebook, Inc. Consumer Privacy User Profile Litigation.
A prolific author and educator,
Daniel has published over 400 legal and technical articles, co-authored books including Cyber Warfare: Understanding the Law, Policy and Technology and Understanding Software, the Internet, Mobile Computing, and the Cloud, and his scholarship has been recognized by Supreme Court Justices and cited in over 500 articles and judicial opinions. He has delivered 600+ keynotes and seminars to judges, executives, attorneys, and government officials worldwide, and is the Editor-in-Chief of the Journal of Law & Cyber Warfare.
Prior to founding Law & Forensics, Daniel built and sold three technology startups. He currently advises AI safety and blockchain analytics companies including Reality Defender and AnChain.ai, and is a Ponemon Fellow at the Ponemon Institute for Information Privacy.
Area of Expertise
Topics
Recon on Trial: The OSINT Operator's Legal Playbook
You scrape a public site. You enumerate subdomains. You grep GitHub for secrets. You curl a misconfigured API. Each one touches a statute. Some have been to the Supreme Court. Most operators don't know which is which — until the preservation letter arrives. A federal-court-qualified expert witness (U.S. v. Sullivan) walks through five live OSINT techniques with real-time legal annotation. Zero lawyer-speak. GitHub release included.
Your AI Agent Needs a Flight Recorder
When an AI agent makes a bad trade, denies a customer, exposes sensitive information, or calls the wrong tool, “the model did it” is not a root-cause analysis. This talk shows AI engineers how to build a forensic flight recorder that can reconstruct exactly what a production agent saw, decided, and did.
Using a failed financial-services agent workflow as a running example, Daniel Garrie will break down an evidence-ready architecture that captures:
Correlation IDs across model calls, retrieval systems, tools, policy checks, and human approvals
Prompt, context, model, tool, and policy versions
Tool inputs, outputs, permissions, exceptions, and side effects
Integrity controls that reveal whether logs or records were altered.
Replay mechanisms—and the limits of deterministic replay
Retention and redaction controls for sensitive customer and business data
Evaluation hooks that distinguish hallucination, retrieval failure, tool misuse, and policy bypass
The goal is not to log everything forever. It is to preserve the minimum reliable evidence needed to debug failures, evaluate agent behavior, investigate incidents, and explain consequential automated actions.
Attendees will leave with a practical agent-event schema, a seven-part logging checklist, and an incident-reconstruction workflow they can adapt to production systems.
Your Agent Just Became Evidence: Logging Agentic Systems for the Day Someone Sues
Your multi-agent system made 400 tool calls, spawned six sub-agents, hit three external APIs, and executed a transaction. Six months later, a lawyer asks you to reconstruct exactly what happened and why. Your traces have a 30-day retention policy. Your prompts aren't versioned. Your model provider silently updated the endpoint. You can't answer.
I'm the person that lawyer hires. I've served as a court-appointed Special Master and expert witness in hundreds of federal and state proceedings, including In Re: Facebook, and I co-created the JAMS AI Dispute Resolution Rules. What I keep finding when I open up agentic systems is that the observability stack engineers already built for debugging is nearly, but not quite, the thing that would have saved them. The gap is usually four or five design decisions, each of which is cheap on day one and impossible to retrofit on day 400.
This is a talk about those decisions, told through real failed reconstructions. Not a compliance lecture. No legal background needed, and none will be assumed.
We'll cover: why span-level tracing is not the same as attribution, and what an agentic trace needs to carry to survive a challenge; the difference between logging what your agent did and logging what your agent knew; why prompt and model versioning is an evidentiary problem before it's an eval problem; the specific failure mode of RAG systems where retrieved context is gone by the time anyone asks what the model saw; and where non-determinism actually becomes a defense rather than a liability.
Who Signed That? Accountability and Evidence When Copilot Agents Act on Their Own
An agent in your D365 environment approves an invoice, adjusts a forecast, or changes a customer record. Nobody typed the instruction. Six months later, a supplier disputes the charge, a regulator asks how the number was derived, or a customer claims a decision was made about them unfairly. The question arrives as simple, and it is not: who did that, and on what basis?
My work is reconstructing what a system did and deciding who is answerable for it, and enterprise Business Applications are increasingly where that question lands.
This session is about the accountability layer that autonomous agents in D365 require and that most deployments do not yet have. Where liability actually sits when an agent acts within delegated authority and gets it wrong. Why "a human approved it" is often not the defense leaders assume. What an agent's action record must contain to be usable when someone disputes it. Which approval and oversight designs hold up under scrutiny and which collapse. And how the contracts underneath your AI stack allocate risk you may not have priced.
Educational and vendor-neutral. No legal background required. Leaders leave with a framework for authorizing agent autonomy that survives the dispute rather than creating it.
When Your Bug Bounty Becomes a Federal Case: Inside U.S. v. Sullivan, from the Government's Expert W
In 2016, Uber's CSO paid ransomware attackers $100,000 and called it a bug bounty. In 2022, a federal jury convicted him. I was the government's expert witness. This is the inside story of how a bug bounty became a federal prosecution — the facts the press got wrong, the operational failures every program can learn from, and the direct lessons for anyone who runs, submits to, or sits inside one of these programs today.
When the School's AI Gets Subpoenaed: What Leaders Must Ask Before They Buy
Your school has deployed an AI tutor, a proctoring tool, or a wellbeing chatbot. A parent asks what the system said to their child, and why. Can you answer? Most schools cannot, and most contracts with their vendors do not require the vendor to answer either.
I have served as a court-appointed expert and Special Master in hundreds of federal and state proceedings, including In Re: Facebook, and I co-created the JAMS AI Dispute Resolution Rules, the first arbitration framework built for AI disputes. Increasingly the systems I am asked to examine are sitting in schools, and they are handling children's data.
This session is for leaders who sign the contract, not for engineers. We look at what schools are actually agreeing to when they procure AI, where the liability sits when an AI tool makes a consequential judgment about a student, what records a school needs to keep and for how long, and which questions to put to a vendor before signing rather than after a complaint arrives.
No legal or technical background assumed. You leave with a procurement question set you can use on your next AI vendor call, and a clear view of where your school is exposed today.
When Smart Contracts Fail: Resolving the Next Generation of Blockchain and AI Disputes
Smart contracts, decentralized autonomous organizations, tokenized assets, blockchain analytics, and AI-enabled financial systems are moving faster than the legal and institutional frameworks designed to govern them. When these systems fail, the resulting disputes rarely fit neatly within traditional litigation or arbitration models.
Who is responsible when an AI agent executes an unauthorized blockchain transaction? How should a tribunal interpret code that conflicts with a written agreement? What evidence is needed to establish control of a wallet, attribution of an on-chain action, or manipulation of a decentralized protocol? Can an arbitrator order effective relief when assets, developers, and participants are distributed across multiple jurisdictions?
Drawing on his experience as a JAMS arbitrator, mediator, special master, computer scientist, digital-forensics expert, and co-creator of the JAMS Artificial Intelligence Dispute Resolution Rules, Daniel Garrie will examine how blockchain and AI disputes are likely to evolve in 2026 and beyond.
The session will use practical scenarios involving smart-contract vulnerabilities, compromised private keys, decentralized governance disputes, tokenized assets, AI agents, cryptocurrency tracing, and cross-border enforcement. Attendees will learn how parties can structure blockchain transactions and dispute-resolution provisions to reduce uncertainty before a conflict occurs.
Key takeaways include:
How courts and arbitral tribunals distinguish contractual intent from automated code execution
The evidence needed to investigate and prove blockchain-related claims
How wallet attribution, blockchain analytics, and digital forensics should be presented in a legal proceeding
When arbitration, litigation, mediation, or emergency relief is most effective
How to draft enforceable dispute-resolution provisions for smart contracts, DAOs, tokenized assets, and AI-enabled transactions
How organizations can preserve blockchain evidence while maintaining defensible chain of custody
What founders, investors, developers, and policymakers should do now to prepare for the coming wave of AI and blockchain disputes
This session will provide attendees with a practical framework for designing systems, agreements, and governance mechanisms that remain effective when decentralized technology encounters centralized legal authority.
Trust Is the Exploit: Deepfakes, AI Agents, and the Forensics of Synthetic Attacks
A convincing video call from the CEO triggers an urgent credential reset. An autonomous AI agent conducts reconnaissance, crafts personalized messages, accesses internal tools, and moves stolen assets through cryptocurrency wallets. Every individual control appears to function—but the attack succeeds because the organization trusts synthetic identities, automated decisions, and fragmented evidence.
This technical panel reconstructs that attack from beginning to end. Experts in synthetic-media detection, agentic AI security, digital forensics, and blockchain intelligence will examine the artifacts left at every stage: media-generation signals, identity and authentication logs, prompts, retrieval context, agent tool calls, endpoint evidence, cloud telemetry, wallet activity, smart-contract interactions, and laundering paths.
Rather than debating whether AI attacks are coming, the panel will show how defenders can detect, contain, investigate, and prove them. Attendees will receive a practical framework for preserving AI evidence, correlating artifacts across systems, distinguishing human actions from autonomous execution, and reaching defensible conclusions when attribution is uncertain.
The AI Audit Trail: Proving What Your Model Knew, Did, and Decided
Organizations are rapidly deploying AI systems that recommend, decide, communicate, and act—but many cannot reconstruct how a consequential output was produced. When an AI decision is challenged by a customer, regulator, executive, court, or incident-response team, model accuracy is no longer enough. The organization must be able to identify the data, model version, prompt, agent action, policy, and human approval that shaped the result.
This panel brings together Daniel Garrie’s experience as an AI dispute-resolution architect, forensic technologist, attorney, and court-appointed neutral with the perspective of a senior applied-AI leader. Using realistic failure scenarios from energy, finance, healthcare, and the public sector, the panel will examine data lineage, model and prompt provenance, agent logging, human oversight, access controls, testing, retention, and AI incident response.
Attendees will leave with a practical AI Evidence Readiness Framework explaining what organizations should preserve, who should own it, how long it should be retained, and how teams can test whether their AI audit trail actually works before an incident occurs.
The $100,000 Bug Bounty That Became a Federal Case: Inside U.S. v. Sullivan
In 2016, hackers stole data relating to 57 million Uber users and drivers and demanded payment. Uber paid $100,000 through its bug-bounty program. The response led to a landmark federal prosecution of a security executive for handling a data breach. Daniel Garrie served as the government’s digital-forensics expert.
Using the public record and the Ninth Circuit’s 2025 opinion, Garrie reconstructs the incident as an evidence timeline: what the attackers accessed, how the payment was characterized, which records existed or were missing, and why response decisions became evidence of obstruction and concealment. He then introduces a “Bounty-or-Breach” framework for triaging extortion presented as vulnerability disclosure.
Attendees leave with five controls: escalation triggers, preservation requirements, payment gates, notification checkpoints, and a defensible handoff among security, legal, and executives. No vendor pitch; no legal background required.
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