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BUILDING TRUST IN THE AGE OF AUTONOMOUS AI

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Exclusive interview Bilal Baig, Vice President, Solutions Engineering, TrendAI™️

Your keynote focuses on the 2026 cybersecurity threat horizon. What are the biggest shifts enterprises should be preparing for over the next 12 to 18 months?

There are three shifts that enterprises need to prepare for urgently. The first is the governance of agentic AI. Agentic AI is moving into the mainstream, and every AI agent introduced into an enterprise environment effectively becomes a new identity. As organisations begin deploying hundreds or even thousands of agents, they will need clear controls around ownership, permissions, accountability, and response. In my view, this will very quickly move from a best practice to a compliance requirement.

The second is visibility. AI has expanded the enterprise attack surface almost overnight. We used to talk about shadow IT; today, we are dealing with shadow AI. Many organisations do not have a clear visibility of who is using AI, which tools are being used, what data is being shared, or whether AI projects are being built with the right governance in place. Establishing visibility of that attack surface thus becomes essential.

The third is vulnerability prioritization and virtual patching. With AI accelerating vulnerability discovery, organisations will face a growing volume of exposures that cannot all be patched immediately. The challenge will be knowing what to prioritise and how to protect critical systems while remediation is underway. This is why virtual patching is becoming relevant again.

Finally, we will see agentic capabilities become more central to defense. If attackers are using agentic AI, defenders will also need AI-powered, agentic security operations across areas such as SIEM, SOAR, detection, and response.

Across our industry these are the shifts that become extremely important over the next 12 to 18 months

A big part of “what’s coming” is agentic AI moving into production. When an autonomous agent can take actions, call tools, and talk to other agents, what new attack surfaces open up that legacy defences were never built to see?

The biggest change is that the attack surface is no longer limited to data, applications, and infrastructure. Enterprises now also need to govern the agents themselves.

Cybercriminals are already using agentic AI to make attacks more scalable and targeted, with different agents handling reconnaissance, phishing, coordination, and data analysis. At the same time, enterprises are introducing autonomous agents that can call tools, access systems, and communicate with other agents. That creates a new layer of risk.

For example, if one agent does not have permission to complete a task, it may interact with another agent that does. Without the right governance, that can bypass traditional security boundaries. The risk is not always intentional or malicious. An agent may simply be trying to complete its assigned goal, but in doing so it can drift into behavior that creates security, compliance, or data exposure risks.

This is why we need stronger governance around agent-to-agent communication. Enterprises need to understand what each agent is allowed to do, what identity it uses, which systems it can access, and who is accountable if something goes wrong. We should think of every agent almost like a new employee: it needs onboarding, permissions, supervision, and accountability.

Agent-to-agent interaction and data integrity are emerging as core risks. Technically, how do you secure trust between autonomous agents, and stop a compromised one from cascading across a workflow?

The first principle is that AI security has to be layered. It cannot start and end at the agent level. Enterprises need controls across the full AI stack, from infrastructure and microservices to LLMs, agents, applications, and data flows. If any one layer is compromised, it can affect the integrity of the wider workflow. This is particularly important as AI-native applications increasingly depend on multiple models, services, APIs, and agent interactions.

The second priority is controlling how agents communicate with each other and with enterprise systems. That means applying guardrails to inspect prompts, responses, behavior, permissions, and outputs in real time. It also means monitoring agent-to-agent communication so that a compromised or misdirected agent cannot collapse across a workflow unchecked. In short, every layer of the AI ecosystem requires its own security controls.

There is no single magic solution that can secure the entire AI environment. Effective security requires layered capabilities across AI guardrails, governance, LLM security, and backend security. At TrendAI™, we combine these capabilities as we work with partners such as Anthropic and NVIDIA to help organizations secure AI from development through deployment.

Most enterprises are layering agentic AI onto existing infrastructure rather than building greenfield. From a solutions engineering standpoint, where do the security gaps typically appear in those hybrid deployments?

The most common gap is visibility. An organization may officially approve one AI tool, but employees and teams may still be using others across the business. That creates a fragmented AI environment where security teams may not know which models are being used, what data is being shared, or whether those tools are sanctioned. This is where the core issue lies.

Once visibility is established, the next challenge is control. Enterprises need to define what each AI system is meant to do, how it should interact with users and systems, what malicious input looks like, and what type of output should be blocked. . Most of the newer top-tier models have some form of AI security guard built in, but the mid-tier models that many organisations rely on do not have those controls.

The issue is not that organisations are moving fast. Innovation should continue. The risk is moving AI projects into production without the right security checks. The better approach is to establish an AI security blueprint and production gates, so that AI applications, LLMs, agents, data flows, and backend systems are assessed before they go live.

Visibility keeps coming up. What does observability actually look like for autonomous systems and how do you monitor and audit decisions an agent makes with no human in the loop?

Observability for autonomous systems has to work across multiple layers. At the first layer, you need visibility into the agent itself – where it is running, what it is doing, and which systems it is interacting with. At the second layer, you need visibility at the gateway level, where communication moves between users, agents, applications, and LLMs. At the third layer, you need visibility into local or enterprise-hosted LLMs, including how they connect to internal systems, data sources, and services. Together this gives you visibility of how an AI whether a chatbot agent or an autonomous agent communicates with the various backend services it draws data from, including on-prem LLMs and how MCP servers are integrated across the ecosystem.

This also extends to internal LLM projects and public AI services such as OpenAI, where guardrails are needed to monitor usage and reduce risk. With TrendAI™, organisations can identify which AI tools are sanctioned or unsanctioned, user interactions, agent behavior, prompt activity, data movement, and potentially malicious commands. Without this level of observability, organisations cannot properly govern autonomous systems.

For auditing, the starting point is a clear blueprint. Every agent should have a defined role, expected behavior, access permissions, and decision framework. In an AI development lifecycle, for example, agents may generate code, test it, scan for vulnerabilities, and prepare it for commit. But the process still needs checkpoints, audit trails, policy enforcement, and human review at critical stages.

The goal is not to slow AI down but to make autonomous activity measurable, auditable, and accountable. Without those checks, agents can create operational, security, and even cost risks, including excessive token consumption or actions that were never intended by the business.

For an organisation just starting to deploy agentic AI, what’s the advice you would give them to first set-up on the security side and to be aware of the most common early mistake that organisations make?

My advice is to start with an AI security blueprint before moving anything into production. Organisations should first define the use case, expected outcome, the systems the AI will interact with, the data it can access, and the controls required across the lifecycle. Security cannot be treated as an afterthought. It has to be built into the design, development, deployment, and monitoring of every AI-native application.

At TrendAI™, we help organisations secure the full AI lifecycle, from defining the use case and building the AI system to deploying it safely into production and governing it once it is live. This is where TrendAI Vision One™ plays an important role, providing an AI security blueprint that gives organisations visibility into which AI tools are running, which are unsanctioned, where AI is being used, and what risks or attacks may be emerging. It also helps monitor user activity, agent behavior, security posture, rate limits, and token consumption, enabling organisations to put the right guardrails in place before deployment and maintain control as AI scales across the enterprise.

The most common mistake is rushing to production without visibility or governance. Many organisations move quickly because the business pressure around AI is high, but they only revisit security after something goes wrong. The better model is to put production gates in place from day one, so AI can scale safely without creating unmanaged risk.

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UAE Enterprises Shift Focus from AI Adoption to Measurable Business Value

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Exclusive interview with Shadi Hatoum, Regional Director MEA at Tealium

The UAE is moving quickly from AI experimentation to deployment. What do you think will determine which organizations actually turn adoption into measurable business value?

The organizations that create measurable value will be those that connect AI to a clearly defined business decision, then give it the right data and operating model to support that decision.

The UAE has already built significant momentum. Boston Consulting Group found that 42% of UAE organizations qualify as AI Leaders, while 37% have reached the scaling stage of AI maturity. The next test is whether that maturity translates into sustained outcomes.

Deploying a model is not the same as changing how a business operates. AI needs trusted, consented, and real-time context around customer identity, behavior, intent, and journey stage. It also needs clear ownership, measurable objectives, and teams that understand how to use the output.

From my perspective, data readiness and organizational adoption will be the two biggest differentiators. The organizations that can put reliable context in front of AI at the moment of decision, and embed the resulting insight into daily workflows, will be best positioned to turn adoption into measurable value.

How much of the current gap between AI ambition and AI impact comes down to fragmented or outdated customer data?

A significant part of the gap comes down to data readiness, although technology alone is not the whole answer. Most organizations do not lack data. Their challenge is that the data is fragmented, difficult to interpret, or unavailable when a decision needs to be made.

Historical data provides useful depth, but AI also needs data in motion. It needs to understand what a customer is doing now, what has changed, what the customer has consented to, and whether the signal belongs to the correct individual or account.

Tealium’s Future of Customer Data research found that 88% of organizations consider real-time data important to achieving business objectives. That is important because an AI system working from yesterday’s profile may produce a technically valid recommendation that is no longer relevant.

The real opportunity is to create a trusted context layer that connects identity, behavior, consent, and intent, then makes that context available to models and decisioning systems while the customer interaction is still taking place.

Does agentic AI create a new governance problem for enterprises, particularly when agents are acting on customer data without constant human intervention?

Agentic AI does not make the principles of governance entirely new, but it raises the stakes and increases the speed at which controls need to operate. When an AI agent can move from recommendation to action, governance can no longer be treated only as a periodic policy or review exercise. It needs to operate within the data flow and decision process itself.

Enterprises need clear controls over which data an agent can access, the purpose for which it can use that data, how identity and consent are verified, which actions it is authorized to take, and when a person must review or approve the outcome.

The key question is not only whether an agent can access a piece of customer data. It is whether the agent should use that data for this customer, for this purpose, at this moment.

That requires trusted data, current consent, auditability, defined action limits, and clear escalation paths. As autonomy increases, accountability needs to become more precise, not less.

Which sectors in the Middle East do you think are furthest ahead in using real-time customer data and AI together effectively?

From my experience across the region, telecommunications and travel, hospitality, and tourism are among the most active and promising sectors because they generate frequent customer signals and have a clear need to respond in the moment.

In a recent discussion with the Chief Commercial Officer of a major telecom operator in the region, we talked about growth as filling a bucket while also stopping the leaks. Acquisition fills the top, but poor service, irrelevant engagement, and unresolved friction allow value to escape through churn. Sustainable growth requires both attracting new customers and protecting the loyal customer base already in the bucket.

This is where AI-supported decisioning can create real value. Trusted, consented, real-time behavioral data can help an operator recognize changes in usage, service issues, digital engagement, and signs of churn. The next best action may be to resolve a problem, recommend a more suitable plan, position a bespoke package at the right time, or avoid making an offer when the customer first needs support.

The objective is not to push more offers. It is to improve relevance and timing. That can support retention, strengthen loyalty, and create opportunities to grow average revenue per user, or ARPU, by responding to what the customer needs in that moment.

Travel, hospitality, and tourism have a similarly strong opportunity. The customer journey moves from inspiration and research to booking, arrival, the in-destination experience, and loyalty. Each stage creates different needs. A traveler facing disruption needs assistance, while a guest already at a destination may value a timely, personalized experience or service.

These sectors show why hyper-personalization at scale depends on trusted, consented behavioral data in motion. The value comes from giving AI initiatives the current context they need to choose the next best action for that specific moment.

At AI Everything Abu Dhabi 2026, what are the biggest changes you are seeing in the conversations enterprises are having compared with a year or two ago?

The biggest change is that enterprises are no longer asking whether they should adopt AI. They are asking how to move it into production, connect it to measurable outcomes, and govern it at scale.

A year or two ago, many conversations centered on experimentation and individual use cases. Today, the questions are more operational. Leaders want to understand how AI will work with their existing data, how agents will receive current customer context, how decisions will be controlled, and how value will be measured beyond a successful pilot.

I am also hearing much more emphasis on adoption. Organizations recognize that a model does not create value by itself. Teams need clear use cases, practical training, shared measures of success, and confidence in the data behind the recommendation.

The conversation has therefore moved from AI capability to AI readiness. That includes the quality of the data foundation, the governance around it, the ability to make decisions in real time, and the people and processes required to turn those decisions into action. For me, that is a sign that the market is becoming more mature and more focused on sustainable business impact.

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AI Is Expanding the Cyberattack Surface and Redefining Resilience

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Exclsuive interview with Fady Richmany, Corporate Vice President and General Manager, Emerging Markets at Commvault

Integrated Media is at GISEC Global Dubai 2026 with Fady Richmany, Corporate Vice President and General Manager, Emerging Markets at Commvault

How is AI changing the attack surface for organizations in the region?

AI is crucial for digital transformation, so we are not debating the importance of AI. I think the UAE is one of the most advanced countries when it comes to adopting technology. The Dubai Government has also announced plans to use agentic AI extensively in the coming years

However, AI comes with a significant amount of data and introduces new types of identities. We are no longer dealing only with human identities but are increasingly dealing with robotic and AI-agent identities that can access data and systems. This creates additional complexity and consequences from an identity and security perspective.

At the same time, AI-powered attacks are another major concern. In the past, a hacker typically needed to be highly technical. Today, with AI, launching sophisticated attacks can become much simpler.

So, AI is important, but it needs to be utilized properly and governed effectively. There needs to be a balance when organizations and large enterprises adopt these technologies.

Why are threat actors increasingly targeting backup and recovery environments?

Backup and recovery are the last line of defence.

When a company is compromised, one of the first things it needs to do is access its backups to recover its data and operations. Hackers understand this. If they want their attacks to be effective, they also need to target the backup environment.

This is where companies like ours, along with many others in the industry, play an important role in making sure that backups remain clean, protected, and safe so organizations can recover when an incident occurs.

How does identity management need to evolve as AI agents and autonomous workloads multiply?

Agentic AI is putting additional pressure on IT because it is creating many robotic identities.

We have also seen attacks where AI has been used with little or no human intervention. This creates an additional security burden, which means these identities need to be properly protected.

Our approach focuses on the recovery side. Organizations need to use the appropriate security technologies available in the market to protect their environments and ensure that the right identities have access to the right resources.

But we also need to address what happens when an identity is compromised. How do you recover the environment? How do you recover the identity infrastructure? That is becoming a very important topic.

What does Cloud Unity offer beyond separate point solutions?

Cloud Unity brings together identity, security, and operational resilience. We combine people and processes and work closely with many of the leading security technology partners in the market.

We integrate information from different technologies and provide identity resiliency to help protect organizations from both sides of the problem.

We also have something called ResOps, or Resiliency Operations. It is a methodology for how organizations should operate. We believe organizations need to educate their people, keep them aware, and make sure everyone understands their role during a crisis. They also need to modernize their processes.

All of this comes together to make sure the environment remains resilient and productive. And this is not a one-time exercise but has been continuously tested – time and again.

How does ResOps help organizations move from reactive security to a more measurable and proactive approach?

In the olden days, we used what we called backup drills. This is particularly common in banking and government, where people would come together every quarter and check whether the backup was working.

But that approach is no longer enough.

Recovering data is not simply about recovering the information. During a cyberattack, you need to recover clean data and ensure that the environment you are restoring is safe. That means organizations need continuous testing. You have to align people, processes, and technology and make testing an ongoing activity.

When a compromise happens, everyone needs to know their role – Who has access? What are the steps that need to be followed? Who is responsible for each action? The ultimate objective is to be able to recover a clean copy of the data and get the organization back into operation as quickly and safely as possible.

What will the new Center of Excellence in Abu Dhabi focus on, and how will it support the UAE’s digital transformation goals?

One of our strategies is to align closely with local authorities, and work together with them. We work very closely with the UAE Cyber Security Council and have developed this initiative together with them.

The purpose of the Center of Excellence is to bring innovation to the region. It will be a center for innovation, with R&D and technology development taking place in the region. It will also be a center for developing local talent. We are partnering with universities and aim to train young Emiratis in areas such as cyber resilience and cyber defense. It will also be a center for awareness.

I would quote what Dr. Mohamed Al Kuwaiti said- the responsibility for cyber defense does not rely on one technology, one individual, or one government. It requires the entire community to work together to fight cyber threats. That is the purpose of the center. It is to bring all these elements together, support the cybersecurity community, and give back to the region. We want to create awareness, talent, and innovation.

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THE AGENTIC AI ERA: RETHINKING CYBER RISK, GOVERNANCE AND RESILIENCE

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Exclusive interview with Bilal Baig, Vice President, Solutions Engineering, Trend Micro

What is Trend Micro showcasing at GISEC Global 2026, and how does it reflect the shift towards proactive, AI-powered cyber risk management?

We are highlighting our unified cybersecurity platform, Trend Vision One, which is designed as a proactive security platform.

AI has shifted how organisations, governments and agencies respond to threats. It is no longer enough to take a reactive approach. Security needs to become proactive, particularly given the speed at which AI is developing.

We are using AI in two ways. First, we are using AI internally within the platform to identify vulnerabilities. Second, we are using AI to protect our customers.

At GISEC, we are showcasing agentic SIEM, agentic SOAR, XDR capabilities and our full-stack AI security platform. We are also highlighting new developments in AI security, including how AI can help protect against vulnerabilities and zero-day attacks.

How has Trend Micro evolved in both using AI for cybersecurity and securing AI systems themselves?

AI has increased the speed at which organisations can move into production. At the same time, both defenders and attackers now have access to AI. The key question is how organisations manage that risk and how quickly they can protect customers and predict an attack before it becomes a breach.

We created Cybertron, an industry-first cybersecurity LLM, and we also work with frontier AI providers including Anthropic, OpenAI and Microsoft. We use frontier AI to consume vulnerability information, while our customers have access to our broader AI security capabilities.

We are now moving into the agentic AI era, where AI agents can make decisions on behalf of humans. These agents can access systems, emulate human behaviour and perform tasks independently.

For us, agentic AI security comes down to four key areas: visibility, observability, governance and response.

Visibility means understanding what is happening. Observability goes a step further by understanding what an action performed by an AI agent could cause. Governance determines how those agents should be controlled, while response is about deciding what action to take.

What new security and governance challenges arise as agentic AI moves from experimentation to enterprise deployment?

One of the biggest questions is whether an agentic AI system should be treated like a human identity or like software.

A software system needs updates, patches and maintenance. A human has an identity, a job and defined responsibilities. Agentic AI combines elements of both.

Organisations therefore need to give AI agents an identity, establish guardrails around what they can do and ensure that someone within the governance structure is responsible for their actions.

If an agent is given additional responsibilities, organisations need to understand how those permissions are managed and eventually removed when they are no longer required.

In an agentic AI environment, every communication and action needs to be considered within a governance framework. Organisations need to look at every interaction, understand its potential outcome and decide whether an action should be allowed to proceed or stopped.

What does the rise of autonomous or rogue AI agents mean for cybersecurity?

We are entering a world where rogue AI agents can become highly sophisticated systems. This means security cannot focus only on whether the underlying AI model is secure. Organisations also need to examine the actions those models are performing and whether those actions could create a cybersecurity problem.

The attack surface is now changing in terms of both scale and sophistication. Attackers have AI capabilities that can help them launch sophisticated attacks much faster.

This means organisations need AI on the defensive side as well. Security solutions need to match that speed and sophistication while ensuring that governance frameworks prevent malicious outcomes.

How should organisations manage the growing number of vulnerabilities identified by AI?

AI and frontier models can identify vulnerabilities that may not have been visible previously. An organisation that once had to manage 30 or 40 patches could suddenly face thousands.

It is not realistic to address every vulnerability in the same way. Organisations need to prioritise based on the risk and importance of their environment.

They need to identify which vulnerabilities are most important for their particular environment rather than simply looking at a vulnerability’s CVE score.

This is where cyber risk exposure management becomes important. Organisations need to understand the risk, the asset and the identity involved, and then decide which security gaps are most important to close.

How is the UAE’s cyber threat landscape changing as AI adoption and digital transformation accelerate?

The UAE is at the forefront of AI transformation. We are seeing multiple initiatives from the UAE Government, including AI training for government employees, government-focused AI initiatives and the introduction of AI education in schools.

There are already AI systems operating within government, so the digital transformation of AI in the UAE is moving forward rapidly.

Our focus is on helping secure that transformation. As AI systems become more interconnected and increasingly make decisions, the attack surface becomes more complicated.

A layered security approach is therefore important, from the large language model and API access through to the decision-making processes of AI agents, while monitoring for malicious activity.

What should organisations consider around security controls and data sovereignty as they expand their cloud and AI environments?

There is sometimes a misconception that moving to the cloud automatically means an organisation is secure. When cloud computing emerged, we often talked about security as a shared responsibility.

The exposure changes as organisations move from on-premises environments to the cloud and then into AI. The same threat can look very different across these environments.

Organisations need to consider where their assets and identities are located and how they will manage security across these different layers.

For highly sensitive environments, including air-gapped networks and systems involving critical data sovereignty, security may need to remain on-premises. In some national security environments, data cannot be processed outside the country.

Trend Micro has Vision One Sovereign and Private Cloud, which extends our AI cybersecurity unified platform to air-gapped and sovereign environments, with a focus on data sovereignty, localisation and air-gapped deployments.

What role does government-industry collaboration play in strengthening national cybersecurity preparedness and resilience?

Government-industry collaboration is extremely important. Working with organisations such as the Cybersecurity Council, national CERTs and government security services brings together different perspectives.

As governments move towards greater use of AI, industry can help secure that journey while governments provide the regulations and governance frameworks needed to manage these systems.

Without close collaboration, it becomes difficult to create policies that reflect what is actually happening in the private sector.

The objective should be to support innovation without overlooking cybersecurity. Technology is developing extremely quickly, particularly AI, so cybersecurity needs to be considered alongside that innovation.

Government and the private sector need to work together to make sure that while organisations remain at the forefront of technological development, they do not overlook the cybersecurity implications.

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