I help organizations align AI governance, IP strategy, data risk, and business execution — so innovation can scale responsibly.
Grounded in law. Focused on defensible outcomes.
From framework to controls your teams actually run.
Complex material taught plainly to any audience.
Deep understanding of AI, data, and the systems beneath.
Judgment built where the stakes were already real.
Design governance that scales with technology and regulation.
Protect, position, and commercialize innovation with clarity.
Executive partnership for responsible, value-driven adoption.
Understand your goals, risks, and current-state capabilities.
Focus on high-impact, high-risk areas that move the needle.
Design policies, controls, and workflows that work in practice.
Measure outcomes, adapt, and evolve with confidence.
I turn legal, technical, and regulatory complexity into clear next steps.
Proactive governance lowers exposure and builds trust.
Practical frameworks help teams move faster with fewer surprises.
Unified strategy. Stronger execution. Better outcomes.
I bring together engineering rigor, military leadership, legal expertise, teaching experience, and executive advisory to help organizations govern innovation with judgment and integrity.
Working at the intersection of governance, innovation, and trust.
Electrical engineering background with a focus on systems, signal processing, and technology.
I engage technical stakeholders in the technical weeds to understand how your systems actually work.
U.S. Navy Reserve cryptologic officer, leading teams and mission-critical operations.
I establish clear processes and roles to accomplish your mission.
Patent attorney advising innovators, founders, and global enterprises.
I turn what the law requires into language your teams can follow and your counsel can defend.
Adjunct professor teaching intellectual property, AI governance, and innovation law.
I teach your board and your builders the same material, each in the vocabulary they already use.
Executive advisor and fractional CAIO helping organizations navigate AI with confidence.
I sit in the executive seat with you and own the decision, not just the recommendation.
HAWK survived two years of expert review, then about sixty hours of AI-assisted cryptanalysis. It withdrew before standardization — which is the process doing its job. The governance lesson is that your architecture has to assume algorithms can fail.
NIST's draft transition plan puts a schedule on retiring today's public-key cryptography: deprecated after 2030, disallowed after 2035. For businesses, 2030 is the date that will actually bite.
Executive Order 14412 describes the whole threat model in one section: adversaries copy encrypted data now and decrypt it later. It changes the question from when quantum arrives to how long your data must stay secret.
Delete the prompt and the influence can remain. Six questions a model-weight governance review should answer — and what the FTC, NYT v. OpenAI, and EU AI Act Article 10 signal about getting them wrong.
A typical LLM system holds nine data stores, each with its own retention, deletion, access controls, and breach surface. When you evaluate an AI vendor, don't ask one question. Ask nine.
Google's framework isn't a replacement for the NIST AI RMF — it's a translation layer for it, speaking the language boards already use. Frameworks don't govern AI. People do.
You do not need a finished program to begin. Governance can start with what you already have today — policies, contracts, and workflows. Let's build a plan that reduces risk, protects the people who trust you with their data, and accelerates responsible innovation.
