Keith Hollender is helping enterprises rethink cybersecurity as AI changes both attack and defense. At Arcova, Keith and his team work with large organizations to solve complex security challenges with a practitioner-led approach built around flexibility, execution, and client trust. Arcova recently won a Fortress Cybersecurity Award for its work helping enterprises strengthen their security programs in a fast-changing threat environment.

In this episode, Russ and Keith discuss Arcova’s evolution from a cybersecurity practice inside Morgan Franklin Consulting to an independent, private equity-backed company. Keith explains how his experience as a cybersecurity buyer inside large financial institutions shaped Arcova’s approach: make engagements easier, stay flexible, avoid unnecessary bureaucracy, and focus on delivering what clients actually need.

The conversation dives into how AI is changing cybersecurity. Keith explains that AI is helping defenders identify and fix vulnerabilities faster, but it is also helping adversaries find new pathways in. That shift is accelerating everything, from threat and vulnerability management to detection engineering, autonomous SOC work, and the need for faster response.

Russ and Keith also discuss why identity has become the new front line. As non-human identities, agents, API keys, and automation expand across enterprises, organizations need better control over who or what has access, what those identities can do, how they are monitored, and how quickly access can be revoked.

The episode also covers third-party risk, supply chain exposure, the Lynx Technology Partners acquisition, continuous risk monitoring, geopolitical risk, global regulation, quantum readiness, and why security strategies may need to evolve every 18 months as AI and quantum computing continue to change what organizations know and what they need to protect.

Topics Covered:

[00:01] Welcome and intro, Keith Hollender and Arcova

[00:32] Arcova’s origin inside Morgan Franklin Consulting

[01:33] Keith’s background inside large financial institutions

[01:54] Building a cybersecurity firm from the buyer’s perspective

[03:20] Management buyout, acquisition, AI practice launch, and rebrand

[04:02] Why the management buyout was the catalyst event

[05:14] What enterprises ask Arcova to solve

[06:12] How AI is changing enterprise security engagements

[06:33] AI, efficiency, and changing operating models

[08:09] The difference between digital transformation and AI transformation

[09:28] How AI affects vulnerabilities and zero-day discovery

[11:16] Old attack methods becoming new again

[12:36] Detection engineering and autonomous SOC work

[13:25] Faster breakout times and faster response needs

[14:20] Why identity is now the front line

[15:13] Non-human identities, access controls, and monitoring

[16:47] Managing AI agents, credentials, and guardrails

[18:46] Third-party and supply chain risk

[19:25] Why third-party risk remains difficult to control

[21:00] Lessons from the Lynx Technology Partners acquisition

[21:17] Continuous monitoring versus annual vendor surveys

[22:47] Geopolitical risk and nation-state threat actors

[25:13] Global regulation, DORA, GDPR, CCPA, and NYDFS

[27:02] Quantum readiness and post-quantum security

[28:49] Why quantum will create another major shift

[30:31] Hardening for what we know while preparing for uncertainty

[31:24] Final thoughts on AI, quantum, and cybersecurity strategy

Mike Maynard has spent decades helping technical B2B companies communicate clearly, reach the right audiences, and turn complex ideas into effective marketing. As Managing Director of Napier, Mike brings a rare mix of engineering, sales, and marketing experience to industries like telecom, semiconductors, industrial automation, and technical B2B. He is also one of BIG’s All-Star Judges, recognized for his thoughtful work reviewing nominations and identifying strong examples of innovation.

In this episode, Russ and Mike discuss how B2B marketing is changing again as AI reshapes content, search, data, personalization, attribution, and go-to-market strategy. Mike explains why AI is already becoming part of nearly every marketing workflow, but also why it is not equally good at everything. For highly technical, original content, human expertise still matters. For personalization at scale, idea generation, and speeding up execution, AI can be incredibly powerful.

The conversation dives deeply into GEO, or generative engine optimization, and how brands can show up in AI-generated answers. Mike explains why good SEO still matters, but why marketers now need to think beyond keywords and start optimizing for prompts, questions, summaries, FAQs, key takeaways, brand mentions, YouTube, LinkedIn articles, newsletters, and high quality content that AI engines can understand and cite.

Russ and Mike also discuss B2B data overload, attribution, incrementality, personalization, customer journey timing, and why many companies still fail to use even the most basic data they collect. Mike explains why marketers should focus less on misleading attribution models and more on whether campaigns actually move people forward in the buying journey.

Along the way, Mike shares lessons from buying Napier just before the dot-com crash, judging BIG nominations, technical content marketing, AI sales coaching, Coachable AI, and why marketers should treat AI like a conversation instead of a one-shot prompt machine.

Topics Covered:

[00:00] Welcome and intro, Mike Maynard, Napier, and BIG judging

[00:57] Mike’s path from electronics engineering to marketing

[02:00] Buying Napier just before the dot-com crash

[03:58] Napier’s focus on technical B2B marketing

[04:30] How the internet changed how engineers find information

[06:02] Technical sales, expertise, and content marketing

[06:51] How AI is changing content marketing

[07:30] Where AI works well and where human experts still matter

[08:33] GEO, AIO, AEO, and getting found in AI engines

[09:28] Why many brands still lack an AI visibility strategy

[10:00] Why strong SEO still helps with GEO

[10:56] Optimizing for prompts instead of keywords

[11:30] Why FAQs, summaries, and key takeaways help AI visibility

[12:22] White hat versus risky GEO tactics

[14:13] Why some AI optimization tactics also help humans

[14:50] Getting your brand into AI-generated answers

[15:33] What Mike learned from testing 1,000 prompts

[15:56] Why Reddit and Wikipedia matter less in niche technical B2B

[16:52] YouTube, multimedia, and AI visibility

[22:11] GEO compared with early SEO and content marketing

[25:09] LinkedIn articles and newsletters as AI visibility opportunities

[27:17] B2B marketers and data overload

[27:57] Using data to identify untapped sales opportunities

[29:30] Companies collecting data but failing to follow up

[31:55] Why Mike cares more about incrementality than attribution

[36:18] Personalization, timing, and customer journey context

[39:14] Coachable AI and AI-powered sales role play

[42:19] The AI prompt Mike recommends for marketers

[43:40] Why AI should be a conversation, not a single prompt

[44:32] Final thoughts on B2B marketing, AI, and judging

Becca Toth is helping redefine CRM for the AI era. At SugarAI, Becca is focused on how AI, CRM, ERP data, and revenue signals can come together to help sales teams make better decisions and take the right next action. SugarCRM recently won a SAMI Award for its work helping organizations organize, understand, and act on the data inside their sales, prospecting, and marketing engines.

In this episode, Russ and Becca discuss how CRM has evolved from a system of record into a system of intelligence, and now toward what Sugar calls precision selling. Becca explains that the old CRM promise of a 360-degree customer view often left users with more dashboards, more reports, and more manual analysis. Today, sellers do not need more data. They need clarity, context, and guidance on what to do next.

The conversation explores why CRM adoption and value often break down at the user level. Salespeople are not always sitting at a desk searching through dashboards. They are in meetings, on the road, working with customers, and trying to decide where to focus their limited attention. Sugar AI is designed to help by surfacing next best actions, preparing sellers for customer conversations, and connecting signals that already exist across the business.

Russ and Becca also discuss the role of ERP data, agentic AI, business signals, customer retention, inventory insights, decision velocity, CFO expectations, and why many organizations are reevaluating their tech stacks as license costs rise and AI changes what software should deliver.

A major theme is that growth is often hiding in the data companies already have, but have not connected. Becca explains that AI should not simply be an “ask me anything” search bar. It should absorb complexity so people can focus on relationships, judgment, creativity, and better customer outcomes.

Topics Covered:

[00:01] Welcome and intro, Becca Toth and SugarAI

[00:55] Sugar’s history and redefining CRM for the AI era

[01:53] Moving from customer data to customer intent

[02:36] Why the old CRM model stopped meeting user needs

[03:38] How CRM has evolved beyond desk-based workflows

[04:26] Bringing mature products forward without alienating customers

[05:06] What precision selling means

[05:32] Shifting from a data problem to a decision problem

[06:30] CRM as a system of record, intelligence, and precision

[07:11] Why many CRM vendors remain dashboard-driven

[08:15] Where CRM pain shows up in organizations

[10:30] Validating AI tools against real business outcomes

[11:41] Finding growth in connected business signals

[12:36] Customer stories and measurable productivity gains

[13:57] Retooling business infrastructure with AI

[15:15] Connecting CRM, ERP data, and AI

[16:42] Replacing task lists with next best actions

[17:05] How Sugar AI changes a salesperson’s daily workflow

[18:40] Customizing AI around each customer’s business

[20:08] Agentic AI and the future of CRM workflows

[20:52] Moving beyond AI as a search bar

[21:30] Decision velocity and faster action

[22:29] How B2B buying behavior is changing

[24:33] ROI conversations with skeptical CFOs

[26:00] How quickly customers see impact

[27:49] Why CRM change often happens during broader business change

[29:13] AI’s impact on marketing, engineering, and software teams

[29:48] How AI lets non-technologists participate in technology work

[31:17] Final thoughts on Sugar AI, and the SAMI Award

Episode Description:
Becca Toth is helping redefine CRM for the AI era. At SugarCRM, Becca is focused on how AI, CRM, ERP data, and revenue signals can come together to help sales teams make better decisions and take the right next action. SugarCRM recently won a SAMI Award for its work helping organizations organize, understand, and act on the data inside their sales, prospecting, and marketing engines.

In this episode, Russ and Becca discuss how CRM has evolved from a system of record into a system of intelligence, and now toward what Sugar calls precision selling. Becca explains that the old CRM promise of a 360-degree customer view often left users with more dashboards, more reports, and more manual analysis. Today, sellers do not need more data. They need clarity, context, and guidance on what to do next.

The conversation explores why CRM adoption and value often break down at the user level. Salespeople are not always sitting at a desk searching through dashboards. They are in meetings, on the road, working with customers, and trying to decide where to focus their limited attention. Sugar AI is designed to help by surfacing next best actions, preparing sellers for customer conversations, and connecting signals that already exist across the business.

Russ and Becca also discuss the role of ERP data, agentic AI, business signals, customer retention, inventory insights, decision velocity, CFO expectations, and why many organizations are reevaluating their tech stacks as license costs rise and AI changes what software should deliver.

A major theme is that growth is often hiding in the data companies already have, but have not connected. Becca explains that AI should not simply be an “ask me anything” search bar. It should absorb complexity so people can focus on relationships, judgment, creativity, and better customer outcomes.

Topics Covered:

[00:01] Welcome and intro, Becca Toth and SugarCRM

[00:55] Sugar’s history and redefining CRM for the AI era

[01:53] Moving from customer data to customer intent

[02:36] Why the old CRM model stopped meeting user needs

[03:38] How CRM has evolved beyond desk-based workflows

[04:26] Bringing mature products forward without alienating customers

[05:06] What precision selling means

[05:32] Shifting from a data problem to a decision problem

[06:30] CRM as a system of record, intelligence, and precision

[07:11] Why many CRM vendors remain dashboard-driven

[08:15] Where CRM pain shows up in organizations

[10:30] Validating AI tools against real business outcomes

[11:41] Finding growth in connected business signals

[12:36] Customer stories and measurable productivity gains

[13:57] Retooling business infrastructure with AI

[15:15] Connecting CRM, ERP data, and AI

[16:42] Replacing task lists with next best actions

[17:05] How Sugar AI changes a salesperson’s daily workflow

[18:40] Customizing AI around each customer’s business

[20:08] Agentic AI and the future of CRM workflows

[20:52] Moving beyond AI as a search bar

[21:30] Decision velocity and faster action

[22:29] How B2B buying behavior is changing

[24:33] ROI conversations with skeptical CFOs

[26:00] How quickly customers see impact

[27:49] Why CRM change often happens during broader business change

[29:13] AI’s impact on marketing, engineering, and software teams

[29:48] How AI lets non-technologists participate in technology work

[31:17] Final thoughts on SugarCRM, Sugar AI, and the SAMI Award

Sanjay Castelino is helping organizations protect sensitive data as AI, SaaS, cloud, remote work, and agentic systems expand the modern attack surface. At Skyhigh Security, Sanjay focuses on Secure Service Edge, data protection, AI governance, and helping enterprises control how people and systems access and use critical information. Skyhigh Security recently won a Fortress Cybersecurity Award for its work protecting organizations from internal and external data threats.

In this episode, Russ and Sanjay discuss why data protection has become central to enterprise security. Companies want to move faster with AI, but they fear sensitive data leakage, unauthorized access, and loss of control over intellectual property, customer information, and regulated data.

Sanjay explains how Skyhigh helps organizations unify protection across web, cloud, SaaS, private applications, endpoints, mobile devices, browsers, and AI services. A key starting point is visibility. Organizations need to know where sensitive data lives before they can protect it, which is why DSPM, or Data Security Posture Management, has become so important.

Russ and Sanjay also discuss shadow AI, secure browser controls, WebSockets, copy and paste risks, and why traditional web protection is not enough for modern AI use. Sanjay shares how Skyhigh can identify sensitive data, apply policies across environments, and block risky actions like copying protected content into public AI tools.

A major theme is zero trust for AI. Sanjay explains that companies can no longer think only about users outside the network coming in. With AI agents operating inside systems at machine speed, security teams must also think inside to inside and inside to outside. That means applying zero trust controls to human and non-human users, specific applications, specific data sets, and specific moments in time.

Topics Covered:

[00:01] Welcome and intro, Sanjay Castelino and Skyhigh Security

[00:31] What Skyhigh does in Secure Service Edge

[01:00] Protecting data from internal and external threats

[01:49] Why data protection became central to Sanjay’s work

[04:18] Explaining value to CISOs with crowded security stacks

[05:00] Secure browser controls without disrupting users

[06:18] Reducing incident resolution time from hours to minutes

[08:01] Why data protection was overlooked during the SSE rush

[10:07] Why data now lives across web, cloud, private apps, and AI

[11:39] DSPM and finding sensitive data across environments

[12:20] Connecting data visibility with policy enforcement

[13:42] What surprises customers after AI governance goes live

[14:30] Why organizations underestimate AI service usage

[15:13] Why WebSockets create new AI data protection challenges

[16:00] Blocking sensitive copy and paste into AI tools

[17:49] Best-of-breed versus platform consolidation

[18:30] How to test whether a platform is truly integrated

[19:00] Processing massive data sets fast enough to protect them

[21:14] Shadow AI and unapproved employee tools

[22:00] Different corporate risk tolerances around AI

[22:53] Zero trust for human and non-human users

[23:22] Rethinking zero trust for agentic AI

[24:00] Inside to inside and inside to outside security models

[25:15] Why agentic AI makes zero trust more urgent

[25:45] Human-targeted attacks and AI-targeted attacks

[26:42] User behavior, impossible travel, and access controls

[27:33] Moving from blocking AI to enabling it safely

[28:19] Final thoughts on secure AI adoption

Eddy Zervigon is helping enterprises prepare for the shift to post quantum cryptography. As CEO of Quantum Xchange, Eddy is focused on helping organizations secure critical data before quantum computing changes the rules of encryption. Quantum Xchange recently won a Fortress Cybersecurity Award for its work helping enterprises build a more agile approach to cryptographic security.

In this episode, Russ and Eddy discuss why post quantum security is not just about choosing a new algorithm. Eddy explains that encryption has worked for decades because organizations could rely on a small number of trusted algorithms and predictable advances in computing power. But with quantum computing and AI advancing together, that model is breaking down.

The conversation dives into crypto agility and why Quantum Xchange believes companies need an architecture that lets them change cryptographic algorithms without bringing down the network. Eddy explains the importance of separating key generation and delivery from the data plane, giving organizations more control over their post quantum journey.

Russ and Eddy also discuss the “harvest now, decrypt later” threat, where adversaries collect encrypted data today and wait until quantum computers can break it in the future. That makes quantum security a current issue, not a distant one. Eddy argues that organizations should start by securing the biggest pipes carrying the most important data, including backup, data center, high value network, healthcare, financial services, and critical infrastructure traffic.

Along the way, Eddy covers NIST standards, federal timelines, cryptographic inventory, FIPS validation, vendor dependence, budget gaps, congressional testimony, and why real crypto agility means being able to hot swap algorithms without creating network downtime.

Topics Covered:

[00:00] Welcome and intro, Eddy Zervigon and Quantum Xchange

[01:06] What Quantum Xchange does

[01:30] NIST, post quantum algorithms, and architectural security

[02:14] Why encryption must keep evolving

[02:50] AI as the brains and quantum as the brawn

[03:33] Eddy’s background in investment banking and mission driven companies

[04:55] Separating cryptographic control from the data plane

[05:35] Why embedded endpoint encryption is no longer enough

[06:30] Why algorithm updates should not require downtime

[07:32] Why the post quantum threat is urgent

[08:21] Harvest now, decrypt later

[09:36] What happens if organizations do nothing

[09:51] Why cryptographic inventory alone may not be enough

[10:30] Securing the biggest data pipes first

[11:37] Why migration is harder than people assume

[11:52] Moving beyond set it and forget it encryption

[13:21] Reducing post quantum migration timelines

[14:10] How often algorithms may need to change

[14:26] NIST algorithms and future uncertainty

[15:42] Change nothing, change everything

[16:08] Comparing crypto control to identity and access management

[17:02] Why network engineers are cautious

[18:00] What the next two years may look like

[19:21] Guidance for smaller and mid-sized businesses

[19:41] Starting with the most sensitive data flows

[21:37] Deciding what needs to be quantum safe first

[22:59] Federal post quantum migration deadlines

[23:27] Standards, validation, FIPS, and common criteria

[24:19] Eddy’s congressional testimony

[25:38] Why budgets need to match timelines

[26:21] Defining real crypto agility

[26:40] Hot swapping algorithms without downtime

[27:41] Final thoughts on crypto agile versus crypto fragile

Sandeep Rachapudi is helping enterprises navigate the shift from digital transformation to AI transformation. As an IT services expert at Tata Consultancy Services, Sandeep has spent more than two decades working across fintech, cloud migration, digital transformation, generative AI, and now agentic AI. He is also one of BIG’s 2025 All-Star Judges, recognized for his commitment to reviewing nominations, providing thoughtful feedback, and helping identify innovation across industries.

In this episode, Russ and Sandeep discuss what it means to judge innovation in a world where every company is trying to prove its value. Sandeep shares how his background in IT services helps him evaluate nominations by looking past the technology itself and asking a more important question: what value did it create?

The conversation also explores what separates strong award submissions from weaker ones. For Sandeep, the best nominations clearly explain the problem, show how the solution was implemented, provide evidence, and demonstrate measurable impact. He especially values entries that include outside links, videos, customer outcomes, and clear proof of how an innovation improved a process, helped a customer, reduced risk, or created business value.

Russ and Sandeep also discuss the current wave of AI adoption. Sandeep explains why companies should not put AI everywhere just because they can. Instead, they need to understand where probabilistic systems make sense, where deterministic systems should remain untouched, and what happens if an AI agent fails. His message is clear: use AI where it can improve decisions, patterns, analytics, fraud detection, operations, and efficiency, but keep humans in the loop where failure could create serious risk.

Along the way, Sandeep discusses recognition, trust signals, cloud transformation, agentic AI, token costs, fintech use cases, kitchen inventory automation, risk assessment, and why human judging still matters in an AI-driven world.

Topics Covered:

[00:00] Welcome and intro, Sandeep Rachapudi and BIG’s All-Star Judges

[01:03] Sandeep’s role at TCS and background in IT services

[01:09] Two decades in fintech, cloud migration, digital transformation, and AI

[01:41] How Sandeep first became involved as a BIG judge

[02:12] Why recognition matters beyond internal company awards

[04:43] Recognition as a trust signal and third party validation

[05:33] Why transparent, online judging matters

[06:46] The commitment behind judging six BIG programs

[07:20] What keeps Sandeep coming back as a judge

[08:55] Why reviewing nominations feels like seeing innovation pitches

[09:40] How Sandeep evaluates award entries

[10:00] Why outside links, videos, and proof points help judges

[11:22] How judging sparks new ideas

[12:06] What separates standout nominations from weaker ones

[12:30] Examples of innovation in sustainability, AI, and customer value

[14:27] Why outcomes matter more than technical complexity

[15:28] The importance of showing clear value

[16:34] How judging across industries helps Sandeep in his own work

[17:06] Learning from nominations and applying those ideas to AI agents

[18:30] Digital transformation, cloud migration, and the next wave of AI

[20:23] What has changed most in enterprise AI over the last year

[22:00] Where AI should and should not be applied

[22:40] Fintech examples: transaction systems, fraud prevention, and analytics

[25:48] AI use cases in operations, kitchens, inventory, and waste reduction

[27:30] Asking what happens if an AI agent fails

[28:28] Human in the loop as the last risk mitigator

[29:16] What listeners should understand about judging and recognition

[31:42] Final thoughts on judging, feedback, and the BIG community

Harshit Kohli is helping enterprises understand where AI, cloud, streaming analytics, and cybersecurity are heading next. As a Senior Technical Account Manager at Amazon Web Services and a GenAI streaming specialist, Harshit works with customers on cloud adoption, artificial intelligence adoption, and real-time data use cases. He is also pursuing a doctorate in artificial intelligence and continues to serve as one of BIG’s All-Star Judges.

In this episode, Russ and Harshit discuss what he is seeing across AI, cybersecurity, MCP, and agentic systems after speaking at industry events including RBLN and the MCP Dev Summit. Harshit explains why companies are moving quickly toward AI, but must also keep security, governance, authorization, and authentication at the center of every deployment.

The conversation dives into model context protocol, or MCP, and how it is evolving beyond simple request and response workflows. Harshit shares how he demonstrated a streaming MCP architecture using Amazon MSK, WebSockets, IAM authorization, and Amazon Bedrock to push real-time context into AI agents. The goal is to help organizations detect anomalies, understand root causes, and act faster when minutes or seconds can carry major business risk.

Russ and Harshit also explore the risks around compromised MCP servers, agent hijacking, shadow AI, least privilege access, sandbox execution, human in the loop validation, and the growing need for governance around AI agents that can now plan, act, execute code, and manage infrastructure.

Along the way, Harshit shares why enterprises should not chase AI for its own sake, why many projects may fail without clear use cases and controls, and why the best companies are not replacing people with AI, but investing in their teams so people can do more with it.

Topics Covered:

[00:00] Welcome and intro, Harshit Kohli and BIG’s All-Star Judges

[00:43] Harshit’s role at AWS and work in GenAI streaming

[01:55] His doctorate in artificial intelligence and industry speaking

[02:19] Key themes from RBLN around AI, cybersecurity, and governance

[03:56] AI-driven phishing volume and security industry response

[04:33] Why governance and security must be built into AI products

[05:44] MCP Dev Summit and the evolution of model context protocol

[06:23] Moving MCP beyond basic request and response

[07:00] Harshit’s streaming MCP demo with Amazon MSK, WebSockets, IAM, and Bedrock

[08:26] Why real-time anomaly detection matters in financial systems

[08:53] Root cause analysis and remediation through AI agents

[10:29] Observability, telemetry, and cost anomaly detection

[12:16] MCP design flaws, exposed servers, and enterprise risk

[12:52] Why AI agents now execute code and manage workflows

[14:41] What a compromised MCP server means

[15:05] Sandbox execution and least privilege access

[17:23] Consent, verification, and human in the loop controls

[18:40] The rise of enterprise AI agents

[19:21] Why agent autonomy creates governance challenges

[20:38] Deploying agents versus deploying applications

[22:38] Agent hijacking and prompt injection risks

[23:00] Poisoned documents, malicious tools, and compromised MCP servers

[25:19] Patch windows, enterprise readiness, and AI cyber risk

[26:30] Shadow AI inside organizations

[26:55] Employees using unapproved AI tools at work

[28:57] Data leakage, compliance violations, and hallucination risk

[30:46] Why some AI projects may be canceled

[31:07] Starting with real use cases instead of forcing AI

[32:44] Whether AI has become infrastructure yet

[33:39] What the best companies are quietly getting right

[34:46] Final thoughts on investing in people while adopting AI

Sasibhushan Rao Chanthati, Senior Software Engineer at Hirekeyz and a two time BIG All Star Judge for Information Technology, joins the Winners' Circle to talk about agentic AI, the FinOps boom, small versus large language models, and his own original research into detecting IT burnout with AI.

Guest info: Sasibhushan Rao Chanthati, Senior Software Engineer, Hirekeyz.

• [00:00] Welcome and congratulations on being a two time BIG All Star Judge
• [01:02] Sasi's role at Hirekeyz and his previous work at T. Rowe Price
• [02:55] What drew Sasi to judging IT nominations in the first place
• [04:41] Task specific agents versus chatbots, the real difference
• [10:13] Small language models versus large frontier models
• [13:25] The hybrid model trend engineers are actually using
• [14:58] Why cheaper AI products often cut corners on security
• [18:23] Customer support automation and where it is heading
• [22:08] The biggest shifts Sasi has seen in the last two years
• [25:12] Why FinOps is becoming mission critical
• [27:00] The origin story behind Sasi's AI burnout detection research
• [29:04] The moment that convinced Sasi this was worth building
• [32:27] Is AI making burnout better or worse
• [37:39] Closing thoughts and congratulations

• After two years judging IT nominations for BIG, Globee, and Stevie, Sasi has learned that the strongest submissions describe a specific mechanism, what was actually built and how it gets used, rather than leaning on company size or mission statements alone.
• AI cost management went from a niche concern to a near universal practice in just two years, with FinOps Foundation research showing adoption jump from 31 percent of organizations in 2024 to 98 percent in 2026, as AI spend joins cloud spend as something every technology leader has to actively manage.
• Sasi's own research into AI driven IT burnout detection, built using vector embeddings and workplace communication analysis, is one of the few systematic attempts to measure a problem the industry has talked about informally for years but rarely tried to quantify.

• Hirekeyz
• Sasibhushan Rao Chanthati on LinkedIn
• Sasi's research on Google Scholar
• Sasi's research on ResearchGate
• Sasi's ORCID

Sasi is a two time BIG All Star Judge for Information Technology. See his full judge profile.

🔗 Subscribe to the Winners' Circle podcast.

Amy Worley is helping business leaders rethink privacy, cybersecurity, AI governance, and data protection as one connected challenge. As a Managing Director at BRG, Amy brings a rare mix of experience as a former trial lawyer, in-house privacy leader, consultant, expert witness, and author of The Confidence Advantage. BRG recently won an AI Excellence Award, recognizing work at the intersection of AI, privacy, cybersecurity, and governance.

In this episode, Russ and Amy explore why AI has made it harder for companies to treat privacy, security, and governance as separate functions. Amy explains how businesses often have legal teams talking about GDPR or HIPAA, cybersecurity teams talking about threat actors and attack surfaces, and AI governance teams working in still another language. Her Confidence by Design framework brings those worlds together through a shared set of principles, common language, and unified risk metrics.

Amy also shares how her career shaped her perspective. She began in law, moved through data privacy and breach response as the internet and privacy statutes evolved, then went in-house to build a GDPR program for a multinational pharmaceutical company. That experience taught her the gap between giving advice and actually building programs that work inside a business.

The conversation also covers what happens during data incidents, why communication and decision authority often break before technical response does, and how Amy uses a “pre-mortem” process to help companies identify what could derail a governance program before it starts.

Russ and Amy also discuss AI deployment, data debt, enterprise LLMs, accountability, board responsibility, chief trust officers, and why digital trust should not be treated as a cost center. Amy’s message is clear: in a digital first, AI powered world, evidence based trust can become a real business advantage.

Topics Covered:

[00:00] Welcome and intro, Amy Worley, BRG, and the AI Excellence Award

[00:22] What BRG does as a multinational expert services firm

[00:53] Amy’s background as a lawyer and privacy professional

[01:11] What a former trial lawyer sees in data breach response

[02:00] Moving from legal advice to building real privacy programs

[03:13] The Confidence Advantage and Confidence by Design

[03:33] Why privacy, cybersecurity, and AI governance need to be unified

[04:00] Building an 11 principle framework across three disciplines

[05:05] What breaks when privacy, security, and AI teams are siloed

[06:00] Creating a common language for executives and risk

[07:05] Whether one team should own the unified governance vision

[07:59] What day one looks like in a data incident or program build

[08:14] Communication rules and decision authority during incidents

[09:00] Using a pre-mortem to identify why a program might fail

[10:07] Common roadblocks: executive understanding and team bandwidth

[10:45] Defining what winning looks like before the work begins

[12:08] What courtroom experience teaches about documenting governance

[13:28] How AI responsibility has shifted from planning to cleanup

[14:15] Why companies now need diagnostics for AI bottlenecks

[15:00] Building agile governance and risk tiers for AI adoption

[16:03] Confidence by Design in thirty seconds

[16:40] Maximizing the value of business data

[17:32] Data debt, enterprise LLMs, and old information resurfacing

[19:49] How to identify who is truly accountable for risk

[20:09] Why AI governance is becoming a board level issue

[21:20] The case for a chief trust officer

[22:18] What leaders should do differently tomorrow

[22:31] Digital trust as a competitive advantage

[23:22] Final thoughts on AI, privacy, cybersecurity, and the future of trust