The Legal AI Operating Layer
The Legal AI Operating Layer:
Moving From AI Assistants to Controlled Workflows
About the Event
Law firms are increasingly using Generative AI for every tasks: legal research, document review, drafting, and knowledge retrieval. As AI systems and agents become more capable, firms are moving beyond individual productivity tools to building repeatable workflows.
As law firms plan for 2027, decisions extend beyond the question of selecting the right AI system. It raises an important question for law firm leaders: How can we control the AI system we use?
Join us for a vendor-neutral discussion on Legal AI bringing together practical business, legal, governance, and engineering perspectives.
What We’ll Explore
Inside the Legal AI operating layer: where AI systems and agents are connected to the firm’s work. Here questions around control can be addressed: How should agents work with firm knowledge and tools? What data can they access and use? What actions require human approval? How can firms evaluate accuracy, reliability, and business value before deploying AI systems?
We've seen that the importance of control looks different depending on where you sit. An attorney is thinking about whether it is reliable to use in their research and filings. A managing partner is concerned whether it impacts professional responsibility and the firm’s promises to its clients. A security, technology or AI leader is worried about how they can show it works or if something goes wrong.
So, we’ll discuss how these questions are shaping the next generation of legal AI - the technologies, governance decisions and implementation approaches to:
Connecting AI to Firm Data: Retrieve and use relevant documents, precedents, and internal knowledge while respecting access permissions, client restrictions, and information governance policies.
Moving From AI Assistants to Agentic Workflows: Turn recurring legal tasks into structured, AI-assisted processes for research, document analysis, contract review, and drafting, with clear responsibilities, approval points, and human oversight.
Evaluating AI Reliability and Readiness: Establish practical methods for testing outputs, verifying sources, identifying errors, measuring workflow performance, and determining when an AI-enabled process is ready for controlled deployment.
Then, we’ll walk through common scenarios where controlled workflows can address challenges that come up in everyday legal work by using our framework made publicly available for you to use:
PriviLAB is an open-source framework developed by Data Strategy Lab to demonstrate how law firms can build controlled AI workflows - that respect access permissions, protect sensitive information, verify results, and keep legal professionals in control of important decisions.
What You Will Take Away
By the end of this webinar, you will understand approaches to:
Connect AI systems to firm knowledge while protecting confidentiality and respecting access permissions;
Improve AI reliability through quality checks, workflow controls, and human oversight;
Prioritize AI workflows to select, test, and implement within your firm.
You’ll also get a practical framework for your next AI investment or workflow decision.
Who Should Attend
This session is designed for law firm leaders who are already exploring or using Generative AI.
We welcome participation from:
CIOs, CTOs, and law firm technology leaders;
managing partners and law firm operations executives;
knowledge management and information governance leaders;
legal risk and compliance professionals; and
other AI innovation and legal engineering professionals.
Speakers
Andrew Tsintsiruk - Founder & Managing Partner, Data Strategy Lab; Lead Architect, PriviLAB
His work focuses on connecting models to governed knowledge, evaluating outputs, and building human decision points into workflows.
Adrian Erlinger - Business AI Strategist, Data Strategy Lab
He helps organizations turn AI experiments into workflows with owners, review paths, and measures of value.
Antonia (Toni) Dumas - Responsible AI & Data Governance, Data Strategy Lab; Attorney (privacy, cyber & AI)
She advises on privacy, security, and trust in digital products and tool adoption.
About Data Strategy Lab
Data Strategy Lab helps knowledge-based organizations in the Washington, DC area move from AI experimentation to practical implementation. We combine AI strategy, workflow redesign, systems and data engineering, systems integration, task-specific evaluation, governance implementation. We also provide community and workforce development to help organizations build reliable AI capabilities that improve productivity, strengthen knowledge management, and support professional judgment.
This session is educational. DSL is not a law firm and does not provide legal advice.