The Tech Trek

Raj Koo, CTO at DTEX, joins The Tech Trek for a sharp conversation on insider risk, shadow AI, and why security teams need a more modern way to think about intent. This episode is worth your time if you are trying to understand how AI is changing cyber risk, why non malicious behavior can still create major exposure, and what it takes to protect the business without slowing down innovation.

Raj explains why the old approach of blocking known bad behavior is no longer enough. As employees bring personal AI tools into the workplace, security teams are dealing with a new reality, one where productivity gains, agentic workflows, and data exposure are all colliding at once.

In this episode

• Why DTEX focuses on inferring intent, not just catching exfiltration

• Why shadow AI is different from shadow IT, and harder to control

• How non malicious employee behavior can become the biggest insider risk category

• Why agentic AI raises the stakes for visibility and governance

• How mature insider risk programs are shrinking response times even as costs rise

Timestamped highlights

• 00:00 Raj Koo on inferring intent in cybersecurity

• 01:59 Why early warning signals matter more than the exfiltration point

• 04:38 The rising cost of insider risk

• 06:25 How shadow AI became a major non malicious risk

• 08:13 Why shadow AI is more complex than shadow IT

• 17:53 Detection times are improving, but the cost problem is getting worse

Standout line

Security has a chance to stop being seen as the function that blocks productivity and start being seen as the function that helps the business adopt better tools safely.

Practical takeaway

If your team is dealing with AI adoption in the wild, start with visibility before judgment. Understand which tools people are using, what they are using them for, and where the real risk sits before defaulting to blanket restrictions.

Link to 2026 Cost of Insider Risks Global Report: https://ponemon.dtex.ai/

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Sumeet Arora, Chief Product Officer at Teradata, joins The Tech Trek for a sharp conversation on the shift from human driven SaaS to agentic software. This episode digs into what changes when software stops just supporting human workflows and starts driving outcomes alongside people, why trust and governance matter more as AI systems take on more responsibility, and what serious companies need to do now to prepare.

This is a practical discussion about where the market actually is, what gets overhyped, and what leaders should focus on beneath the noise. Sumeet lays out a clear view of the emerging enterprise stack, from knowledge and context to agents, governance, and outcomes. He also explains why the winners may not be the loudest companies in AI, but the ones that get their data, knowledge, and operating model right.

In this episode
• Why agentic software is a real shift, but still in its early stages• What trust, governance, and explainability need to look like in an AI first enterprise• How software companies should rethink product strategy for agents as well as humans• Why every employee may need to become a manager of AI agents• Why knowledge infrastructure could matter more than the agent layer itself

Timestamped highlights
• 00:45 Teradata’s role in helping enterprises become autonomous• 02:34 Where we really are in the agentic AI maturity curve• 10:16 How software shifts from workflow centric to outcome centric• 16:17 Why every employee may need an AI workforce• 21:57 The skill gap between enterprise users and agentic adoption• 24:48 Why knowledge, not just agents, will define the winners

Standout line

“The fundamental winners will be ones who get the knowledge fabric correct.”

Practical takeaway
If you are building for an AI driven future, do not start with agents alone. Start with trusted knowledge, usable context, clear policies, and systems that can explain decisions. The companies that treat agentic AI as a stack, not a feature, will be in a much stronger position.

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Victor Fang, CEO and Founder of Anchain AI, joins The Tech Trek for a timely conversation on crypto crime, AI driven fraud, and what financial institutions need to understand as digital assets move closer to the mainstream. This episode is worth your time if you care about cybersecurity, compliance, crypto risk, anti money laundering, or where agentic AI is starting to reshape investigation work.

This conversation goes beyond headlines. Victor breaks down how bad actors are using generative AI for phishing, identity fraud, exploit development, and ransomware, then explains how defenders are using AI, graph intelligence, and agent workflows to fight back. It is a sharp look at the collision of crypto, cybersecurity, regulation, and AI infrastructure.

In this episode

• What crypto crime actually looks like today, from exchange hacks to romance scams and ransomware

• Why crypto risk now extends well beyond crypto native users

• How financial institutions, regulators, and compliance teams are adapting

• Where AI is helping attackers move faster, and where it is giving defenders an edge

• Why agentic workflows and MCP powered investigation tools could change this category fast

Timestamped highlights

• 00:00 Victor Fang on crypto crime, AI versus AI, and agentic AML

• 00:53 What Anchain AI does and why blockchain investigation is becoming more important

• 01:56 How generative AI is already being used in crypto crime and phishing

• 06:30 What banks, regulators, and AML teams need to understand about crypto adoption

• 10:44 Why Victor believes AI can give defenders the advantage

• 16:17 How Anchain uses blockchain data, graph intelligence, and agent workflows to investigate faster

• 22:04 Why the company’s MCP server could extend beyond crypto into KYC and financial applications

• 25:21 What the next wave of agent driven security and investigation might look like

One standout idea from the conversation, crypto is much closer to you than you think.

Practical takeaways

• Crypto risk is no longer a niche issue, it is increasingly tied to broader fraud, ransomware, and financial crime

• AI is accelerating both offense and defense, which raises the bar for security and compliance teams

• Agentic investigation workflows could dramatically reduce manual work in AML, fraud, and cyber operations

• Companies building in regulated spaces need infrastructure that can handle both speed and scrutiny

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Cam Crow, Director of Data and Analytics at Vacatia, joins The Tech Trek to unpack what happens when a startup outgrows informal ways of working. This episode looks at how data teams can introduce project management frameworks without killing speed, how to manage stakeholder demand as complexity rises, and why the right operating model matters even more as AI begins to reshape analytics work.

Cam shares a practical view from the middle of real growth, from startup scrappiness to acquisitions, migrations, and a much wider stakeholder base. He explains when process becomes necessary, how to build trust during that shift, and where AI is starting to change both delivery workflows and the future of business insights.

In this episode

• Why early stage teams should add process cautiously, not by default

• The moment speed and quality start breaking under too many competing requests

• How public communication and domain based stakeholder channels reduce friction

• Why planning routines matter as much for stakeholders as they do for the data team

• Where AI fits today, from faster delivery to semantic layers that support better answers

Highlights

00:00 Cam Crowe joins the show to discuss project management frameworks through the lens of data, startup growth, and stakeholder alignment

01:58 Why Cam resisted formal sprint planning in the startup phase and why that made sense at the time

05:58 The tipping point where too many priorities start hurting both velocity and quality

11:49 How moving conversations out of direct messages and into domain channels changed team operations

15:03 Inside the two week development cycle and the planning week that keeps stakeholders engaged

21:08 How Cam is thinking about AI, semantic layers, and the future of on demand analytics

A standout idea from this conversation, process should be added conservatively, only when the business truly needs it.

Practical takeaways

• Do not formalize too early, but do not wait until the system is already breaking

• Make prioritization visible once demand exceeds capacity

• Use shared channels instead of one to one communication to reduce bottlenecks

• Build stakeholder rituals into the operating model, not just team rituals

• Treat AI readiness as an infrastructure challenge, not just a tooling decision

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Deep Sogani, SVP and Group Data Management Officer at Datasite, joins The Tech Trek to unpack why data governance, lineage, and business process design have become mission critical in the age of AI. This conversation gets past the surface level AI hype and into the operational reality, how companies actually build trustworthy systems, where AI initiatives break down, and why strong data foundations now shape business outcomes in real time.

This episode explores the shift from downstream analytics to data that actively drives live decisions, workflows, and automation. Deep explains why many AI projects fail before the model even matters, how business architecture should lead technical design, and why human oversight still matters in high stakes environments.

In this episode

• Why AI has made data governance and data lineage far more operational

• Why business process clarity matters before data architecture or tooling decisions

• How real time AI changes the demands on data quality and system design

• Where agentic AI fits, from workflow automation to more advanced decision support

• Why human judgment still matters in AI systems shaped by risk, ethics, and security

Timestamped highlights

• 01:47 Why AI raises the stakes for governance, lineage, and trust in data

• 04:57 Why business architecture has to lead before technical design

• 09:11 The progression from predictive models to agentic AI workflows

• 17:55 Why the human in the loop is still essential

• 21:16 What makes an AI project worth prioritizing

• 26:06 What has changed, and what has not, in AI related change management

Standout line
“Business architecture and business thinking should dictate the what and the why, and the data architecture is the how part which needs to follow.”

Practical takeaway
If you are evaluating AI inside the enterprise, do not start with the tool. Start with the business problem, the workflow, the decision risk, and the quality of the data behind it. Strong models on the wrong problem still fail.

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Suresh Martha, Head of Data Driven Innovation and Analytics at EMD Serono, joins The Tech Trek for a practical conversation on what leadership looks like when your team is asked to take on new technical capabilities. This episode is about extending team impact, evaluating new tools, building credibility with stakeholders, and leading through change without pretending to be the deepest expert in every domain.

For data leaders, analytics managers, technology executives, and operators, this conversation gets into the real work behind capability building. Suresh breaks down how to assess whether a new technology is worth pursuing, when to start with a pilot, how to upskill internal talent, and how to hire for skills your team does not yet have.

In this episode

• How to evaluate whether a new tool or technology actually adds business value

• Why small pilots help leaders build trust before asking for larger investment

• What it takes to lead technical work you have not personally done yourself

• How to hire for capabilities your team does not yet have

• Why business context and data knowledge still matter as much as technical depth

Timestamped highlights

00:04 Extending technical impact as a leader when new capabilities land on your team

03:37 A simple framework for evaluating new tools, investment, and fit

05:28 Hiring for skills your team does not yet have

07:44 Upskilling as a leader so you can guide the work with confidence

12:06 Managing experts whose technical depth goes beyond your own

15:21 Making room for learning and experimentation while still delivering

Standout line

As long as I understand the intricacies and can explain that, that is what matters, especially for a leader.

A practical takeaway

Start small. Pick a real business problem. Run a focused pilot. Measure the outcome. Earn the right to scale.

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Sourish Samanta, Director AI and ML at Advance Auto Parts, joins The Tech Trek for a grounded conversation on where machine learning still creates the most business value, where generative AI fits, and why many teams are chasing the wrong solution. This episode is worth your time if you want a clearer view of how serious operators think about AI strategy, product delivery, and practical use cases that can ship now.

This conversation cuts through the noise around AI and gets back to first principles. Sourish explains why machine learning remains the foundation behind today’s AI wave, how to choose between deterministic and creative systems, and what it actually takes to build production ready products that solve real business problems.

In this episode:

Why machine learning is still the core layer behind modern AI

When to use machine learning, when to use generative AI, and when simple analytics is enough

What a real product mindset looks like for AI and ML teams

How pod based teams can ship faster with better cross functional alignment

Why AI and ML talent need to spend time continuously reskilling

Timestamped highlights:

00:00 Why machine learning remains the foundation of today’s AI stack

01:57 The difference between ML teams, AI teams, and agent focused workflows

05:56 Choosing the right solve, from forecasting and inventory to creative content generation

10:09 The product mindset required to turn AI ideas into working systems

13:51 Why some business problems need analytics, not AI

15:52 Why AI teams need to spend part of their time learning, testing, and staying current

Standout line:

AI is not the strategy. Solving the right problem is.

Practical takeaway:

If you are leading an AI initiative, start by classifying the problem. If the outcome needs consistency, prediction, or forecasting, machine learning may be the better path. If the outcome needs creativity or flexible generation, generative AI may be a better fit. And in some cases, the best answer is still a clean dashboard and strong analytics.

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Shamoon Siddiqui, CEO and Founder of Human Friendly Robotics, joins The Tech Trek to break down what it really takes to bring robotics into construction. This is not a futuristic thought experiment. It is a grounded conversation about where robots can create value now, why construction has lagged so badly on productivity, and how focused automation could reshape one of the world’s biggest industries.

At the center of the discussion is Tyler, a tile laying robot built as a practical entry point into construction automation. Shamoon explains why repeatable workflows matter, where human skill still wins, and how robotics can improve speed, safety, and job site economics without needing to look like a science fiction demo.

In this episode
• Why construction productivity has moved backward while other industries have surged ahead• Why tiling is the right entry point for construction robotics• How Human Friendly Robotics thinks about deployment, rentals, and product iteration• Where robots can reduce hidden job site injuries tied to repetitive strain• Why the long game is much bigger than tile, with plumbing, electrical, and HVAC in sight

Timestamped highlights
00:35 Why construction is the right market for robotics right now03:56 The bigger shift from humans moving atoms to machines handling more physical work08:29 Why the business model is built around rentals, not one time equipment sales10:24 The wedge strategy today and the larger vision across licensed trades12:12 The overlooked safety problem of repetitive strain in construction20:44 Why useful robots matter more than robots built for flashy demos

“Version one is not going to be as good as version five, but if you continue to rent it from us, we can make sure you get version five when it’s ready.”

Practical takeaway
The smartest automation wedge is not the flashiest one. Start with repetitive, measurable work, prove productivity gains in the real world, and expand from there.

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#ConstructionTech #Robotics #Automation #ai #FutureOfWork

Mary Elizabeth Porray, Global Vice Chair Client Technology and COO, Growth and Innovation at EY, joins The Tech Trek for a grounded conversation about what it actually takes to operationalize emerging technologies inside a global enterprise. This episode goes past the AI hype cycle and into the real work of adoption, change management, process redesign, workforce trust, and leadership in ambiguity.

A lot of companies are asking what AI can do. Fewer are asking what needs to change for AI to actually work. Mary Elizabeth shares how EY is thinking about experimentation, employee experience, guardrails, internal adoption, and the cultural shifts required to move from curiosity to real impact.

In this episode

Why culture, not technology, is often the biggest blocker to emerging tech adoption

Why AI is not a magic wand, but can help teams solve problems in a different way

How leaders can identify the right starting points by listening for real pain points

Why productivity gains have to create psychological space, not just more work

How affinity groups, storytelling, and visible leadership help drive adoption

Timestamped highlights

01:58 Why cultural norms often slow down emerging technology adoption

03:25 AI hype, false expectations, and what the technology can realistically change

05:55 The mental load of AI at work, and why EY created Thrive Time

11:20 Why AI pilots need to go deeper than surface level experimentation

15:19 How AI is creating a shared language between business and technology teams

29:29 How storytelling, affinity groups, and positive momentum help people lean in

One line that sticks: AI is not something you dabble in.

A practical takeaway

The best place to start is not with the flashiest use case. It is with a real pain point. If a process should take one week and actually takes eight, that is a signal worth following.

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Michael White, Co founder and CEO of Multiply, joins the show to talk about the path from engineering leadership to the CEO seat, and what it really takes to build in a high trust, high complexity market. If you are thinking about founder readiness, leadership growth, or where AI creates real value in fintech, this episode gets into the parts that matter.

Michael shares how early entrepreneurial instincts showed up long before Multiply, what changed as he moved from builder to company leader, and why some of the most important skills in leadership have less to do with code and more to do with communication, conviction, and influence. He also breaks down how Multiply is using AI to improve the mortgage experience without removing the human element people still need in a major financial decision.

In this episode:

• The mindset shift from engineer to CEO

• Why leadership becomes a form of sales

• How founder timing can be an advantage, not a delay

• Where AI fits in the mortgage process, and where it does not

• Why startups can move faster than legacy players in AI adoption

Timestamped highlights

00:43 What Multiply is building, and why an AI native mortgage company sees a better path to homeownership

01:47 The childhood business story that hinted at an entrepreneurial future

06:20 What changed in the move from engineering leadership to founder and CEO

08:45 Why so much of leadership comes down to influence, alignment, and selling the vision

17:19 Why mortgages are such a strong use case for AI, and why the back office is the real opportunity

22:39 The startup advantage in AI, speed, focus, and freedom from legacy systems

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