The AI Forecast: Data and AI in the Cloud Era

You can talk an AI model into breaking its own rules. Sometimes, all it takes is knowing what to say.

Dr. Peter Garraghan has spent years studying how attackers exploit AI systems, and some of his findings are surprisingly human. AI models can be manipulated through language, using techniques that resemble the social engineering tactics cybersecurity professionals have dealt with for decades.

In this episode of The AI Forecast, Paul Muller sits down with Dr. Peter Garraghan, Chair Professor at Lancaster University and Founder and Chief Science Officer of MindGuard, to explore the emerging AI attack surface and what enterprises need to understand to secure increasingly capable systems.

As Peter puts it, “AI is software, and all software has security vulnerabilities.” AI models introduce new complications for cybersecurity; their behavior is difficult to inspect, and their hidden capabilities can give attackers unexpected ways to exploit systems connected to sensitive enterprise data.

Inside the mind of an AI security researcher:

How attackers can manipulate AI models through language and social engineering

Why AI introduces new cybersecurity vulnerabilities and attack surfaces

How humanizing AI can create misplaced trust in its capabilities

What AI red teaming reveals about model vulnerabilities

Why traditional security tools struggle with AI behavior

How enterprises can test AI systems before deploying them

The risks of connecting autonomous AI agents to sensitive business data

What CISOs should prioritize when building an AI security strategy

The next cybersecurity vulnerability may be hiding in a conversation with your AI. Peter makes the case for finding it before someone else does.

Want to hear more about securing AI agents? Check out Ep 95 | AI Agents Move Fast. Can Security Keep Up? with Alex Bovee to learn why autonomous systems are forcing enterprises to rethink zero trust and real-time security. 

Stay in touch with Peter:

Peter Garraghan on LinkedIn: https://www.linkedin.com/in/pgarraghan/ 

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One developer burned through 300 million AI tokens in a single week. The payoff was far less impressive.

For Todd Trevillion, it's a striking example of what can happen when AI adoption outpaces the controls needed to manage it. And with autonomous AI agents beginning to make decisions on behalf of businesses, the stakes are getting higher.

Recorded at EVOLVE26 Singapore, this episode of The AI Forecast follows Paul Muller as he sits down with Todd Trevillion, technology entrepreneur and co-founder of Citadel Edge, to explore whether enterprises are truly ready for agentic AI and what it takes to deploy the technology responsibly.

Working with organizations in highly regulated industries, Todd has seen how quickly AI ambitions can run into operational reality. He questions whether enterprises are prepared to give autonomous agents greater control when the consequences of their actions can be difficult to predict.

Paul and Todd dive into:

Why agentic AI introduces new risks for enterprise security and governance

What happens when autonomous agents make decisions on behalf of businesses

How uncontrolled token consumption can undermine AI ROI

Why data sovereignty matters in regulated industries

Why AI observability and human oversight remain essential

How to balance AI innovation with enterprise accountability

Todd raises an uncomfortable possibility: what happens when AI agents start making decisions based on other agents’ decisions, with no human watching closely enough to catch a mistake? For enterprises, the potential blast radius could extend far beyond a bad answer.

If you're responsible for enterprise AI strategy or governance, this episode will help you think more critically about the authority you're giving AI agents and the safeguards needed to keep them under control.

Want to hear more about responsible AI adoption? Check out Ep 81 | AI Guardrails: How to Govern AI Without Slowing Innovation.

Stay in touch with Todd:

Todd Trevillion on LinkedIn: https://www.linkedin.com/in/toddtrevillion/

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Like and subscribe to The AI Forecast, sponsored by Cloudera, to stay up to date on the latest episodes in the EVOLVE26 series. You can also watch the video version on The AI Forecast.

The AI worked in the sandbox. Then it met the real world.

David Hardoon learned that lesson during an ATM optimization project designed to improve cash-delivery routes. Once deployed, the system collided with traffic and unpredictable schedules, which are the realities that face the people doing the work.

Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with David Hardoon, Head of Advanced AI at Accenture, to explore why the human side of AI transformation can determine whether promising technology ever delivers value in production. 

Paul and David discuss:

Why technically successful AI projects can fail when they meet real workflows

What AI leaders can learn by going directly to the people doing the work

Why workforce design should be part of AI transformation from the beginning

How agentic AI could reshape career paths and management structures

Why familiar change-management principles matter again in the AI era

How organizations can balance AI innovation with practical implementation

David also reflects on developing the FEAT principles for responsible AI at the Monetary Authority of Singapore and takes a musical detour into Lestrum, his early research connecting patterns in music with brain activity. Both experiences illustrate his belief that AI works best when grounded in how people actually think and operate.

If you’re leading AI transformation, this episode will push you to look beyond the technology and ask whether your organization and its people are ready for what comes next.

Stay in touch with David:

David Hardoon’s website: www.davidroihardoon.com

David Hardoon on LinkedIn: https://www.linkedin.com/in/davidrh/

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Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 Singapore series and stay up to date on the latest conversations about enterprise data and AI.

Your AI agent has credentials. Now, what happens when it goes off script?

An agent can take action and change course before a traditional security review even begins. For Alex Bovee, identity is only the beginning of the security conversation.

In this episode of The AI Forecast, Paul Muller sits down with Alex Bovee, co-founder and CEO of C1 and longtime cybersecurity and identity leader, to explore how autonomous AI agents are changing enterprise security.

Alex argues that zero trust was built for a world where people remained behind the keyboard. AI agents introduce non-deterministic behavior and can relentlessly optimize toward a goal, sometimes finding routes their creators never anticipated.

Paul and Alex’s discussion examines:

Why AI agents require a new approach to identity and access control

How autonomous agents change the zero-trust security model

Why static permissions struggle at machine speed

What real-time enforcement looks like for AI agents

How organizations can identify behavioral drift

How governed “golden paths” can support secure AI adoption

As AI agents take on more work across the enterprise, security will have to keep pace. If you’re responsible for enterprise security, this episode will show you how to keep pace with AI agents operating at machine speed without creating controls that people will work around. 

Stay in touch with Alex:

Alex Bovee on LinkedIn: https://www.linkedin.com/in/alexbovee/

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An AI project can deliver a great prediction and still fail the business.

Rajesh Kelvalkar has seen it happen firsthand. In one mining project, the model worked, but the team discovered that the downstream business process wasn’t configured to act consistently on its recommendations. The technology had done its job, but the transformation hadn’t.

Recorded at EVOLVE26 Singapore, this episode of The AI Forecast brings Paul Muller together with Rajesh Kelvalkar, Senior Advisor at Tech Data APJ, to explore what more than 20 years of transformation work has taught him about putting AI into practice. 

Rajesh argues that organizations often approach transformation as a technology initiative, even though much of the work happens elsewhere in the business. Clear ownership and measurable outcomes determine whether an AI pilot becomes part of operations or disappears when the project team moves on.

Paul and Rajesh explore:

Why pilots built on curated data can stumble in production

How repeatable business processes create strong AI use cases

What logistics and financial services can teach us about scaling AI

Why reusable data assets matter beyond a single pilot

How to measure AI against business outcomes

Why AI models need continued monitoring after deployment

Rajesh also makes the case for treating data as a product. Projects have an end date, but products have ownership and continue to evolve. That mindset becomes especially important for AI systems, where changing conditions can quickly affect the performance of a model that worked well at launch.

His advice for leaders planning the next phase of AI starts with the outcome. Identify where AI can influence an end-to-end business process, prove value in a focused use case, and build the foundations that allow successful ideas to be reused elsewhere.

If you’re responsible for AI transformation, this episode will help you think beyond the pilot and build AI capabilities designed to scale and sustain.

Stay in touch with Rajesh:

Rajesh Kelvalkar on LinkedIn: https://www.linkedin.com/in/rajesh-kelvalkar-6b33452/

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Like and subscribe to The AI Forecast, sponsored by Cloudera, to follow our EVOLVE26 Singapore series and stay up to date on the latest conversations about enterprise data and AI.