Why Security Teams Are Being Asked to Do Three New Jobs - and What to Do About It

Guest: Rob Lee, Chief AI Officer and Chief of Research at SANS Institute

Host: Seth Earley, CEO at Earley Information Science

Published on: March 27, 2026

In this episode, Seth Earley speaks with Rob Lee, Chief AI Officer and Chief of Research at SANS Institute, about why AI governance is broken in most organizations - and what it actually takes to fix it. They explore why security teams are being asked to simultaneously govern, adopt, and defend AI, why the default framework of no is driving shadow IT rather than preventing risk, and what a practical reset of AI governance actually looks like. Rob also shares why agents should be treated like workers rather than software, and why executives cannot afford to outsource their understanding of AI to anyone else.

Key Takeaways:

• Security teams are now being asked to do three new jobs at once - evaluate AI tools for the organization, drive their own AI transformation, and manage governance and regulatory compliance.
• The default framework of no does not prevent AI use - it drives it underground, creating shadow IT that is far harder to monitor and control than sanctioned tools.
• Governance needs a stoplight model - green means experiment freely, yellow means involve security as a lifeguard, red means stop - with the default answer being yes unless there is a clear reason to say no.
• AI governance documents written before generative AI arrived are already outdated - most say nothing about agentic workflows, human-in-the-loop requirements, or connector permissions.
• Agents should be treated like workers, not software - they reason, improvise, and operate 24-7, which means they require the same zero-trust principles, oversight structures, and ethical guardrails as human employees.
• Executives cannot outsource their understanding of AI to security teams - AI literacy at the C-suite level is a competitive requirement, not an optional capability.
• Good governance is not about documenting every possible bad outcome - it is about establishing overarching goals and building a culture of trust with enough guardrails to prevent the truly stupid risks.

Insightful Quotes:

"The framework security teams are using is a framework of no. And that framework of no is causing people to use AI secretly, regardless of what the security team says." - Rob Lee

"An agent in the future - and some organizations are already treating it this way - is a worker. Everything you ask about governing agents, replace that with a human who just got hired. The same rules apply." - Rob Lee

"You can't automate what you don't understand - and with agents, the stakes are even higher. An agentic mistake isn't a wrong paragraph, it's a blocked critical system." - Seth Earley

Tune in to discover how security and executive leaders can move from a governance posture of restriction to one that enables innovation, manages real risk, and keeps organizations competitive in the age of agentic AI.

Links:

LinkedIn: https://www.linkedin.com/in/leerob/

Website: https://www.sans.org

Sponsor: Vector - https://www.vktr.com/

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

Accuracy, Trust, and the Interface Revolution: How AI is Transforming Legal Workflows

Guest: Mike Anderson, Chief Product Officer, Filevine

Host: Seth Earley, CEO at Earley Information Science

Published on: March 20, 2026

In this episode, Seth Earley speaks with Mike Anderson, Chief Product Officer at Filevine, about what it takes to bring AI into one of the most demanding and high-stakes environments in the enterprise - legal operations. They explore why AI will not replace attorneys but will dramatically extend what legal professionals can accomplish, how real-time deposition analysis is transforming courtroom preparation, and why information architecture remains the critical foundation beneath every AI capability. Mike also shares why the interface - not the model - is the biggest unlock AI offers the legal industry.

Key Takeaways:

• AI will not replace attorneys or paralegals - legal services are already severely undersupplied, and AI's role is to extend what existing professionals can accomplish.
• The billable hour model is evolving, but the bigger opportunity is eliminating non-billable administrative burden so attorneys can focus on higher-order legal thinking.
• Real-time deposition analysis - live transcription cross-referenced against case files - is one of the most powerful and practical AI applications in legal today.
• Boolean search cannot be replaced in legal because accountability for document populations requires transparent, auditable logic that external parties can evaluate.
• Effective AI in legal requires three information retrieval lenses: semantic search, Boolean search, and attribute-based filtering - all three are necessary.
• Information architecture - defining the is-ness and about-ness of legal objects like matters, contracts, depositions, and clients - remains the foundation for AI to work accurately.
• The interface is the single biggest unlock AI offers legal professionals - the ability to ask a question in natural language rather than navigate complex click paths changes everything.

Insightful Quotes:

"The demand for legal services already outpaces supply, and it has for some time. We should be talking about the productivity and extensibility of legal professionals - not obsolescence." - Mike Anderson

"If only I had this analysis of the deposition during the deposition. That one customer comment kicked off an entire depositions platform for us." - Mike Anderson

"You still need the is-ness and about-ness. The interface changes, but the underlying information architecture is still what makes AI work correctly." - Seth Earley

Tune in to discover how legal teams are moving past AI skepticism and building the foundations that make AI accurate, trustworthy, and transformative in practice.

Links

LinkedIn: https://www.linkedin.com/in/michael-anderson-374299163/

Website: https://www.filevine.com

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

From Vision to Value: How Leaders Can Close the Gap Between AI Ambition and Operational Reality

Guest: Brian Stafford, CEO at Diligent

Host: Seth Earley, CEO at Earley Information Science

Published on: March 9, 2026

In this episode, Seth Earley speaks with Brian Stafford, CEO of Diligent, a $700 million global software and AI company focused on governance, risk, and compliance. They explore why most organizations understand that AI is transformative but still struggle with the how of actually getting there, and what it takes to move beyond pilots into real operational change. Brian shares how Diligent is helping clients in compliance, audit, and risk functions do more with less through AI-wired software and agents, and why context, leadership, and process understanding are the real drivers of successful AI transformation.

Key Takeaways:

• Most organizations have crossed from asking what AI can do to struggling with how to actually execute and drive measurable transformation.
• Calling initiatives pilots gives organizations an excuse to fail - framing AI as transformation from the start changes accountability and outcomes.
• AI maturity is less about sector or company size and more about the quality and commitment of executive leadership driving change.
• Compliance, risk, and audit functions face a structural mandate - increasing obligations with flat or shrinking budgets - making AI adoption a necessity, not a choice.
• Agents should be thought of like a smart new associate - trained gradually, checked in with frequently at first, then trusted to operate with more autonomy over time.
• Context is the key differentiator for AI solutions - partners who already understand your domain, regulatory environment, and workflows will deliver faster, better outcomes.
• AI-native employees who are intellectually curious and fluent in modern tools can deliver 5 to 10 times the output of peers who resist adopting new capabilities.

Insightful Quotes:

"I hate the term pilot. Pilot gives organizations the license to call something unsuccessful. You're not piloting a transformation - you're either driving it or you're not." - Brian Stafford

"Most of our clients don't care if I ever said the word agent. They care about an outcome. The technology is just what helps deliver it." - Brian Stafford

"You can't automate what you don't understand. And once you do understand it, agents change everything - but the process clarity has to come first." - Seth Earley

Tune in to discover how forward-thinking leaders are closing the gap between AI ambition and real operational impact across governance, risk, and compliance functions.

Links

LinkedIn: https://www.linkedin.com/in/brian-k-stafford/

Website: https://www.diligent.com

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

This episode welcomes Sujay Dutta and Siddharth Ragagopal, co-authors of Data as the Fourth Pillar. With extensive experience guiding global organizations on aligning data strategy with real-world business outcomes, Sujay (based in Stockholm) and Siddharth (based in the Netherlands) offer deep insights into AI adoption, data governance, and scaling artificial intelligence responsibly. Hosted by Seth Earley, the conversation explores how businesses can move beyond AI experimentation and develop a mature, impactful data strategy.

Key Takeaways:

• AI Is More Than Technology: AI impacts people, processes, and data—not just IT. Leaders must approach AI holistically.
• Not Every Problem Needs AI: Business leaders should carefully evaluate which challenges truly require AI solutions, and distinguish between traditional AI and generative AI use cases.
• Overcoming Pilot Mode: Successful organizations plan experimentation as part of a longer maturity journey, connecting short-term MVPs to strategic goals.
• The Supply and Demand Gap: Bridging business needs (demand) and technical capabilities (supply) is essential for effective AI integration.
• Stages of AI Maturity: The episode introduces a three-stage maturity model—Foundational, Scaled, and Automated—and explains how organizations can assess their position.
• Data Quality Is Contextual: Data quality requirements should be based on the needs of specific use cases, recognizing dimensions like completeness, timeliness, and relevance.
• Human Factor Is Crucial: Organizational structure, culture, and incentive models must support AI adoption. Preparing people for AI is as important as preparing AI for people.
• Cross-functional Collaboration: Embedding AI and data practices into broader business strategy, and fostering collaboration between business and IT teams, helps avoid siloed efforts.
• Next AI Opportunities: Productivity gains are just the beginning; capturing tacit knowledge and reimagining business processes will drive greater value in coming years.

Featured Quote from the Show:

Tune in to learn how to build a solid data foundation, avoid common AI pitfalls, and prepare your organization—and your people—for the future of intelligent business.

Links

LinkedIn: https://www.linkedin.com/in/sujaydutta

LinkedIn: https://www.linkedin.com/in/sidd-rajagopal/

Website: https://datathefourthpillar.com

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

In this episode of the Earley AI Podcast, host Seth Earley welcomes Krishna Rangasayee, Founder and CEO of SiMa.ai, for a grounded conversation on what it takes to make AI work in real world environments. The discussion focuses on moving beyond hype to address the practical challenges of deploying AI systems that are efficient, scalable, and reliable at the edge.
Krishna brings decades of experience across hardware, software, and AI systems design. He shares why many AI initiatives struggle outside controlled environments and how organizations must rethink architecture, performance, and context when deploying AI closer to where data is created and decisions are made. The episode explores why efficiency is not just a cost concern but a core enabler of real time intelligence across industries.

Key Takeaways from this Episode:
Common misconceptions about AI readiness and why scaling models alone does not lead to success

Why edge AI is critical for real time decision making, latency reduction, and operational reliability

How efficiency at the hardware and system level unlocks new AI use cases
The importance of aligning AI architecture with real world constraints such as power, bandwidth, and deployment conditions

Why organizations must rethink the balance between cloud and edge computing

How leadership and culture influence whether AI experimentation turns into production impact

Insightful Quotes from the Show:
"AI success is not about chasing bigger models. It is about understanding the environment where AI actually has to operate and designing systems that work within real constraints." - Seth Earley

"If you want AI to deliver value in the real world, efficiency has to be designed in from the start. Otherwise, intelligence never makes it past the lab." - Krishna Rangasayee

Links
LinkedIn: https://www.linkedin.com/in/krishnarangasayee/
Website: https://sima.ai

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

In this episode, host Seth Earley welcomes Brandon Lucia, CEO of Efficient Computer, for a deep dive into how AI advancements are reshaping the future of computing—particularly with a focus on energy efficiency, sustainable infrastructure, and real-world applications.

Brandon Lucia brings almost 20 years of experience in computer architecture, having served as an academic at Carnegie Mellon University and led significant research at the boundary of hardware and software innovation. He and his team have pioneered a new kind of hardware architecture designed to drastically reduce power consumption for AI workloads without sacrificing performance or versatility. Their work has far-reaching implications for data centers, edge AI, robotics, automotive, and large-scale infrastructure monitoring.

Key Takeaways from this Episode:

• AI’s energy demands are accelerating rapidly and require rethinking not just bigger models, but architectural efficiency at every level.
• Effective AI infrastructure goes beyond mathematical optimization (like linear algebra); it includes real-world complexity and physical deployment.
• Specialized hardware architectures (CPU, GPU) are evolving, but general-purpose solutions with built-in efficiency—like those from Efficient Computer—can unlock new application domains.
• Edge computing and “physical AI” (as distinguished from legacy IoT) require extremely efficient processing to enable long device lifetimes and advanced capabilities.
• Efficient Computer’s chips offer exponential gains in energy efficiency compared to market-leading CPUs and embedded GPUs—sometimes up to hundreds of times better.
• Enterprises should focus on hardware-software co-design and apply principles like Amdahl’s Law: you are limited by what you can’t optimize, so balancing all types of computation is critical.
• Fine-grained personalization and retraining of AI at the edge will be increasingly important for future applications.
• Organizations that deal in manufacturing, logistics, automotive, infrastructure, or robotics stand to benefit greatly from advances in efficient hardware and architecture.

Insightful Quote from the Show:

"We're not going to meet these energy requirements with the existing hardware and software—we have to change." - Seth Earley

"We are vastly ahead of our competition when it comes to energy consumption. Batteries last longer. You can do more under a power cap. You're not limited by thermal constraints. Those convert directly into capabilities into lifetime. So you can do more than you could do today." - Brandon Lucia

Tune in for a conversation that not only explores the technical side of AI hardware, but also the practical, business, and societal impacts of powering tomorrow’s intelligent systems with greater efficiency.

Links

LinkedIn: https://www.linkedin.com/in/brandon-lucia-0767792/

Website: https://www.efficient.computer/

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

In this episode of the Earley AI Podcast, host Seth Earley sits down with Forrest Zeisler, co-founder and Chief Technology Officer at Jobber. With years of experience building technology for service professionals, Forrest Zeisler has played a pivotal role in empowering small businesses—from landscapers and plumbers to cleaners and contractors—to harness AI and automation for streamlined operations and growth.

Discover how Forrest Zeisler and his team scaled Jobber from three customers to over 300,000, delivering more than $100 billion in services, and learn how their journey demonstrates the transformative impact AI can have on businesses of all sizes.

Key Takeaways:

• Small businesses can benefit enormously from AI, especially for reducing administrative tasks and boosting productivity.
• Adopting new technology isn't just about features—it's about building trust and reliability for the end user.
• Jobber’s growth began with direct customer conversations, leading to a highly configurable platform supporting over 55 industry verticals.
• The journey from manual onboarding and white-glove service to sophisticated self-serve and AI-driven automations took years of iteration and customer feedback.
• Integrating AI isn’t just about chatbots or flashy features; the real impact comes from making technology disappear in the background, allowing users to focus on their craft.
• Reliable automation, rooted in real customer behavior and best practices, is key to driving widespread adoption of AI across industries.
• Building trust with AI systems should mirror how you onboard new employees: review, supervise, and gradually increase autonomy as reliability is proven.
• Orchestrating multiple AI models and agents allows platforms like Jobber to deliver context-aware, intelligent assistance that feels human and personalized.

Insightful Quotes:

"AI is beginning to simplify that work and reduce administrative overhead and reduce those efforts and help small companies provide more consistent and more efficient and more reliable results." - Seth Earley

“Our goal is not to stick a lot of chatbots in front of our customers. It's to make Jobber just magically always seem like it knows what you need when you need it. We want to measure our success by how little we're sticking in front of our customers.”- Forrest Zeisler

Tune in for a behind-the-scenes look at building scalable, reliable AI for small business—and the lessons you can apply whether you're an entrepreneur or driving digital transformation in a larger enterprise.

Links

LinkedIn: https://www.linkedin.com/in/forrestzeisler/

Website: https://www.getjobber.com

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book

Join us for a compelling episode of the Earley AI Podcast as host Seth Earley sits down with George Swetlitz, CEO and Co-Founder of RightResponse AI. George brings decades of expertise in natural language technologies, enterprise AI adoption, and building advanced models to solve real business challenges—especially in the realm of customer engagement, feedback, and competitive analysis.

Tune in as George shares how AI-powered systems are changing the way organizations capture, understand, and act on customer feedback to deliver more relevant, personalized, and valuable experiences. He discusses why sounding “human” isn’t enough, the importance of contextual relevance, and how to transform the review response process at scale for both efficiency and revenue growth.

Key Takeaways:

• Relevance Over Sounding Human: The real power of AI in customer experience lies in delivering contextually relevant responses, not just in mimicking human conversation.
• Granular Sentiment Analysis: Advanced AI systems can break down reviews into meaningful phrases, better identify true intent and sentiment (even with sarcasm), and map feedback to business KPIs.
• Building Fact Repositories: Onboarding AI involves creating a dynamic library of facts drawn from reviews, responses, and website content, enabling responses that are tailored to specific, high-value customer concerns.
• Operational Impact at Scale: Large organizations can redeploy significant resources by automating repetitive review responses, freeing up staff to focus on complex, high-touch customer problems.
• Personalized Review Requests: AI can personalize review requests by incorporating context from customer interactions, dramatically improving conversion rates and generating more insightful customer feedback.
• Competitive Insights: AI-driven analysis of both your reviews and your competitors’ can highlight where you’re outperforming or falling short—especially at the hyperlocal level.
• Future of AI in CX: As AI models become more advanced, onboarding and implementation will become smoother, and the quality of customer engagement will only improve.

Insightful Quote:

“What you’re trying to do with AI is get the best of both worlds. You’re trying to be relevant to somebody in the space or in the place that they’re in… The best customer service rep would do that. And now, at scale, AI can help organizations truly meet customers where they are.” George Swetlitz

Listen now and discover how leveraging AI in customer feedback can transform both experience and outcomes!

Links:

LinkedIn: https://www.linkedin.com/in/george-swetlitz-7b43812/

Website: https://www.rightresponseai.com

Thanks to our sponsors:

• VKTR
• Earley Information Science
• AI Powered Enterprise Book