Legalweek 2025 - Good Times

 

Watch the video of pics from Legalweek in NYC 2025 - My 20th Legalweek/Legaltech

Fun Times at Legalweek

Under whose umbrella? Navigating the Specialized Needs of Information Governance and Legal Operations

Under Whose Umbrella: Navigating the Specialized Needs of Information Governance and Legal Operations

In the corporate world, two umbrellas Information Governance (IG) and Legal Operations (Legal Ops) shelter a sprawling array of specialized needs, each vying for attention in an era of digital transformation. These domains aren’t just buzzwords; they are frameworks that manage risk, ensure compliance, and unlock value from data. But what lies beneath each umbrella? How do their scopes intersect, and who holds the handle, be it the CIO, CISO, CTO, or even the CEO? Let’s unpack this, spotlighting eDiscovery as a pivotal element, alongside cybersecurity, computer forensics, IT, Legal IT, data protection, records management, archival of records, data governance, data privacy, risk management, and compliance.

Under the Information Governance Umbrella

Information Governance is the backbone of an organization’s data strategy, a holistic approach to managing information assets across their lifecycle. It’s about control, accountability, and foresight. Beneath this umbrella, specialized needs emerge:

• eDiscovery: The process of identifying, collecting, and producing electronically stored information (ESI) for legal proceedings. IG ensures eDiscovery is defensible, think retention policies that prevent spoliation or data mapping that locates ESI fast. It’s the foundation for litigation readiness. I have argued for years that eDiscovery is a subset of an information governance program.
• Cybersecurity: Protecting data from breaches is non-negotiable. IG defines access controls and encryption standards, aligning with security protocols to safeguard sensitive information.
• Computer Forensics: When incidents occur, IG supports forensic analysis, tracking data trails to uncover breaches or misuse, often feeding into eDiscovery efforts.
• IT: The operational engine, IT executes IG policies, deploying systems for storage, retrieval, and security. It’s the plumbing beneath the strategy.
• Data Protection: IG ensures compliance with laws like GDPR or CCPA, setting rules for data handling and breach response.
• Records Management: From creation to disposal, IG governs how records are classified, stored, and purged, balancing utility with regulatory mandates.
• Archival of Records: Long-term preservation falls here, ensuring historical data remains accessible yet secure, often for audits or litigation.
• Data Governance: A subset of IG, this focuses on data quality, consistency, and ownership, critical for analytics and compliance.
• Data Privacy: IG overlaps with privacy, enforcing policies that protect personal data and manage consent.
• Risk Management: By identifying data vulnerabilities, IG mitigates financial and reputational risks.
• Compliance: The glue that binds it all, IG ensures adherence to industry standards and regulations.

Who Holds the IG Umbrella? Typically, the Chief Information Officer (CIO) or Chief Data Officer (CDO), if the role exists, wields control, given their oversight of IT and data strategy. However, the Chief Information Security Officer (CISO) often co-owns cybersecurity and data protection, while the Chief Compliance Officer (CCO) may weigh in on regulatory alignment. In some firms, the CEO steps in when IG escalates to enterprise-wide risk, signaling its strategic weight.

Under the Legal Operations Umbrella

Legal Operations, meanwhile, is the business engine of the legal department, optimizing processes, managing costs, and aligning legal work with corporate goals. Its umbrella covers needs that often overlap with IG but serve a distinct purpose:

• eDiscovery: Here, Legal Ops focuses on execution, managing vendors, streamlining review workflows, and cutting costs. While IG sets the stage, Legal Ops runs the play, often leveraging third-party solutions for efficiency.
• Cybersecurity: Legal Ops collaborates with IG to address breach fallout, think litigation risk or regulatory fines, rather than owning prevention.
• Computer Forensics: Legal Ops taps forensics for evidence in disputes or investigations, relying on IG’s groundwork.
• Legal IT: A specialized subset of IT, Legal Ops owns tech stacks like eDiscovery platforms, contract management systems, and case analytics, tools that boost legal productivity.
• Data Protection: Legal Ops ensures legal processes (e.g., contracts, NDAs) comply with protection laws, leaning on IG for policy.
• Records Management: Legal Ops manages legal-specific records, court filings, legal hold obligations, agreements, while IG handles broader retention.
• Archival of Records: Legal Ops archives case files for future reference, often outsourcing to IG’s systems.
• Data Governance: Less central here, but Legal Ops uses IG’s data standards for legal analytics or reporting.
• Data Privacy: Legal Ops navigates privacy in legal contexts, for example, client data in discovery, relying on IG’s framework.
• Risk Management: Legal Ops mitigates legal risks (e.g., litigation exposure), distinct from IG’s broader data risks.
• Compliance: Legal Ops ensures legal activities meet regulatory and ethical standards, overlapping with IG’s compliance arm.

Who Holds the Legal Ops Umbrella? The General Counsel (GC) or Chief Legal Officer (CLO) typically oversees Legal Ops, with a Legal Operations Manager handling day-to-day execution. The Chief Technology Officer (CTO) may influence Legal IT, but control rarely shifts outside legal leadership unless escalated to the CEO for budget or strategic calls.

The Nexus Debate: Where’s the Line?

The overlap between IG and Legal Ops, especially with eDiscovery, sparks debate. IG builds the infrastructure (e.g., data retention for eDiscovery), while Legal Ops drives its application (e.g., review efficiency). But the nexus blurs with shared needs:

• Cybersecurity and Data Privacy: IG owns the policies; Legal Ops handles legal fallout. Who’s accountable when a breach triggers litigation?
• Legal IT vs. IT: Legal Ops demands tailored tools, but IG’s IT backbone supports them. Does the CIO or CLO dictate tech priorities?
• Compliance: Both chase it, but IG’s scope is enterprise-wide, while Legal Ops is legal-centric. Who resolves conflicts?

This tension often hinges on control. If the CIO or CISO dominates IG, Legal Ops may feel sidelined, relying on IT without steering it. If the GC holds sway, IG might bend to legal priorities, neglecting broader data needs. The CEO becomes the tiebreaker when silos clash, but proactive firms appoint a Chief Data Officer (CDO) or Chief Privacy Officer (CPO) to bridge the gap, aligning both umbrellas under a unified vision.

Beyond the List: Additional Factors

• AI and Analytics: Tools like Needle (from The Project Consultant) sit at the IG-Legal Ops intersection, analyzing data for legal insights, whose budget funds them?
• Vendor Management: Legal Ops often owns eDiscovery vendors, but IG may oversee data security vendors, another overlap point.
• Cultural Buy-In: Neither umbrella works without stakeholder alignment, does the C-suite or department heads drive adoption?

Conclusion: A Shared Canopy

Information Governance and Legal Operations aren’t rivals…they’re partners under a shared canopy. IG provides the data foundation; Legal Ops turns it into action. eDiscovery exemplifies this dance, IG ensures readiness, Legal Ops delivers results. Cybersecurity, IT, and the rest weave through both, but their ownership depends on who holds the umbrella, and how well they collaborate. As disputes over nexus persist, the answer isn’t one leader (CIO, CISO, or GC), but a coalition, often led by the CEO or a hybrid role like the CDO. Under this umbrella, the future will more likely be protected.

 

How Better Data Drives Superior Generative AI Results

 

How Better Data Drives Superior Generative AI Results:

In the rush to develop and deploy generative AI solutions, we often overlook a fundamental truth: the quality of AI outputs is directly determined by the quality of its training data. Data that has been accurately classified prior to training creates a foundation for more reliable, useful, and trustworthy AI systems.

Why Classification Matters

When training large language models (LLMs), poorly classified data introduces noise and inconsistencies that the model inevitably learns and reproduces.

Consider these impacts:

1. Contextual Understanding: Precisely classified data helps models understand when and where specific information applies, reducing irrelevant or inappropriate responses.

2. Reduced Hallucinations: Well-classified training data creates clearer boundaries for an AI's knowledge, making it less likely to "hallucinate" or fabricate information when operating outside its knowledge base.

3. Enhanced Specialization: Models trained on accurately classified domain-specific data demonstrate superior performance in specialized fields like legal, medical, or technical domains.

4. Improved Reasoning: Clear classification patterns in training data translate to better logical reasoning capabilities in the resulting AI.

The Business Case:

Organizations investing in data classification before AI training are seeing tangible benefits:

40-60% reduction in model retraining cycles

Significantly higher accuracy in domain-specific applications

Reduced risk of compliance issues and reputational damage

More efficient use of computing resources during training

Looking Forward:

As we move from the "early adoption" phase of generative AI to more mature implementations, the competitive advantage will increasingly belong to those who prioritize data quality over quantity. The most successful AI implementations will be built on foundations of meticulously classified, contextually rich datasets.

#GenerativeAI #DataQuality #MachineLearning #AIStrategy #DataClassification #ediscovery #informationgovernance #dataprotection #dataprivacy #edrm #aceds #arma #iapp #compliance #grc #legalweek2025

Bulidng the Roads for Generative AI – Using ETL (Extract, Transform, Load) What is an ETL tool?

 

Bulidng the Roads for Generative AI – Using ETL (Extract, Transform, Load)

What is an ETL tool?

When looking at generative artificial intelligence, an ETL tool is a software solution or process that handles “Extract, Transform, Load” operations, adapted to the unique needs of AI systems and their supporting agents. Traditionally used in data warehousing, ETL has evolved in the AI era to play a critical role in preparing and managing data for generative models and the agentic frameworks that orchestrate their activities.

In a prior LinkedIn post, I used the metaphor of cars, roads, and traffic regulations, likening them to development of AI chatbots. In furtherance of that analogy, let’s take a look at ETL.

ETL Defined: Extract, Transform, Load

1. Extract: Pulling raw data from various sources, databases, APIs, text files, social media, or even unstructured outputs from generative AI itself (e.g., bot-generated text or images). Think of this as gathering the "fuel" for the AI "car."
2. Transform: Cleaning, structuring, and enriching that data to make it usable for AI models or agents. This might involve normalizing text, removing biases, tagging metadata, or converting formats, essentially tuning the "engine" so the car runs smoothly.
3. Load: Delivering the processed data into a destination, such as a training dataset for a generative AI model, a knowledge base for an agent, or a storage system for downstream use. This is like parking the car on the "road" where it can be accessed or deployed.

In the generative AI and agentic world, ETL isn’t just about moving data, it’s about enabling bots (the AI "cars") and agents (the "roads") to function effectively while adhering to AI governance (the "streetlights and road signs").

ETL in the Generative AI Context

Generative AI models, like GPTs or image generators, rely on massive, high-quality datasets to produce coherent outputs. ETL tools ensure that the data feeding these models is fit for purpose. For example:

• Extract: An ETL tool might scrape web data, pull user prompts from an X feed, or collect outputs from a bot’s prior runs.
• Transform: It could filter out noise (e.g., irrelevant or toxic content), standardize formats (e.g., turning PDFs into plain text), or enrich data with context (e.g., adding sentiment labels).
• Load: The processed data is then fed into the AI’s training pipeline or a real-time inference system, ready for the bot to generate responses.

Without ETL, generative AI would be like a car with no fuel, or worse, fuel that clogs the engine. The bot might "drive" (generate outputs), but it’d be erratic, biased, or stuck in a ditch of bad data.

ETL and Agentic Tools: Building the Roads

Agentic tools, autonomous systems that manage workflows, coordinate multiple AI models, or interact with environments, are the "roads" in our metaphor. They rely on ETL to keep traffic flowing smoothly. Here’s how:

• Extract: Agents need real-time data to act, like a customer service agent pulling live chat logs or a research agent fetching the latest papers. ETL tools extract this dynamically.
• Transform: Agents often work with multiple bots or systems, so ETL harmonizes disparate data (e.g., converting a generative AI’s text output into a structured JSON for an agent to parse). It’s like paving a road to connect different cities.
• Load: ETL delivers the transformed data to the agent’s decision engine or memory bank, enabling it to orchestrate tasks, like routing a bot’s output to the right user or triggering another AI process.

For instance, an agent managing a fleet of generative AI bots (e.g., one writes copy, another design’s images) uses ETL to ensure all inputs and outputs align, much like a highway system keeps cars moving in sync.

AI Governance: The Streetlights and Road Signs

ETL tools also intersect with AI governance, ensuring the "cars" (bots) and "roads" (agents) operate safely and legally. Governance elements, like data privacy laws, ethical guidelines, or bias audits, rely on ETL to enforce compliance:

• Extract: Only pulling data that meets regulatory standards (e.g., GDPR-compliant sources).
• Transform: Anonymizing sensitive info, flagging biased content, or adding traceability tags, akin to installing road signs that say “Speed Limit 55” or “No U-Turn.”
• Load: Storing data in secure, auditable systems, ensuring the AI’s "journey" can be tracked and justified, like streetlights illuminating the path for accountability.

Without ETL, governance would be blind, unable to monitor or steer the AI traffic.

Examples of ETL Tools in This Space

• Traditional ETL Adapted: Tools like Apache NiFi, Talend, or Informatica are being repurposed to handle AI data pipelines, extracting from cloud sources and transforming for model training.
• AI-Specific ETL: Platforms like Hugging Face’s Datasets or Google’s Dataflow cater to generative AI, offering pre-built transformations for text, images, or multimodal data.
• Agentic ETL: Frameworks like Needle or  LangChain include ETL-like components to manage data flows between agents and bots, ensuring seamless "road" conditions.

The Big Picture: ETL as the Mechanic’s Shop

In our analogy, ETL is the mechanic’s shop, tuning the cars (bots), paving the roads (agents), and installing the streetlights (governance). It’s not glamorous, but it’s indispensable. Just as early automobiles needed mechanics to keep them roadworthy, generative AI and its agentic ecosystem depend on ETL to turn raw potential into reliable performance. As we race down this digital highway, ETL tools are the unsung heroes ensuring we don’t stall, and hopefully not crash. along the way.

Legal Operations at a Crossroads: How Corporate Legal Teams Will Continue to Drive Innovation and Implement Digital Transformation in 2025

 Corporate legal teams are at a pivotal juncture, focusing on innovation and digital transformation for 2025. A recent survey by iManage, LegalMation, and Neota Logic highlights key trends:

• Measuring Success: Legal operations are developing metrics to assess their effectiveness, emphasizing the importance of data-driven decision-making.

  iManage

• Generative AI Integration: While generative AI offers significant potential, teams face challenges in its deployment, including data security concerns and the need for specialized expertise.

  Law.com

• Innovation Promotion: There's a concerted effort to foster innovation within legal departments, aiming to enhance efficiency and adapt to evolving business needs.

  Arika Plan Advisors

• Automation and Contract Management: Prioritizing automation and improving contract management processes are central to streamlining operations and reducing manual workloads.

  iManage

The survey also notes that 93% of respondents believe the role of legal operations professionals has expanded, reflecting their growing influence in driving organizational change.

iManage

In summary, corporate legal teams are proactively embracing technological advancements and innovative practices to navigate the complexities of the modern legal landscape.

https://www.law.com/legaltechnews/2024/12/17/legal-operations-at-a-crossroads-how-corporate-legal-teams-will-continue-to-drive-innovation-and-implement-digital-transformation-in-2025/?slreturn=20241227184503

The Power of ‘Other People's Time’ (OPT) and ‘Other People's Money’ (OPM) - book authored by Joe Bartolo, J.D.

 The Power of ‘Other People's Time’ (OPT) and ‘Other People's Money’ (OPM)

“The Power of ‘Other People's Time’ (OPT) and ‘Other People's Money’ (OPM)" reveals the secrets of harnessing external resources to supercharge success, whether for a business or personal goals. The book dives into practical strategies for leveraging the expertise and effort of others (OPT) alongside financial resources from external sources (OPM), emphasizing ethical approaches and responsibility. Through case studies of industry giants like Amazon and Tesla, it showcases how great leaders balance time, capital, and trust. It's an essential read for anyone seeking to multiply their impact by maximizing what they can achieve with the support of others.

 

Treacherous Technology: Generative AI and Quantum Computers - A Discussion of the risks posed by innovative technologies

Treacherous Technology  

A book I recently authored, entitled Treacherous Technology, discussing risks posed by innovation such as generative AI, and quantum computers.  An examination of threats to data security, data privacy, trust, and to intellectual -property.  Includes a look at the history of major hackings, and resulting disruption caused by data breaches. Available for purchase on Amazon at the link provided above.