By Mohammad Ismail, Vice President for EMEA at Cequence Security
Agentic AI has moved from hype to prototype remarkably quickly. Across the GCC, organizations are actively piloting AI agents to automate workflows, make better use of internal data, and interact with a variety of systems. The intent to adopt is clear. What’s less clear is how hard it is to move from experimentation to production.
Regardless of industry or scale, a consistent pattern is emerging across the region. The challenges aren’t about whether AI is valuable, they’re about whether it can be deployed safely and at enterprise scale. The following recurring pain points reveal a set of structural barriers that are shaping how AI initiatives move from pilot to production.
Security First
Security risk is the biggest blocker to agentic AI adoption. This concern carries particular weight in the region, where recent data shows the average cost of a data breach for businesses in the Middle East has reached approximately $7.2 million in 2025.
To make AI agents enterprise-ready, organizations must address core security challenges from the outset. This includes enforcing strict controls to prevent unauthorized use, eliminating over-permissioned agents, and ensuring AI interactions with applications and sensitive data remain governed and visible.
These are not theoretical considerations. They reflect practical realities organizations already anticipate as AI agents gain autonomy. Enterprise readiness therefore depends on proactively identifying and mitigating these risks—before agents are deployed at scale—rather than reacting to incidents after the fact.
Governance and Compliance
The GCC has strengthened its data governance landscape through landmark regulations such as the UAE’s Federal Decree-Law No. 45 of 2021 on Personal Data Protection and Saudi Arabia’s Personal Data Protection Law (PDPL), raising the compliance bar for how organizations manage and secure data.
Against this backdrop, the need for auditability, traceability, and explainability in AI systems has never been more important. Organisations must be able to clearly address who approved an agent’s access, what actions it performed, and why it made specific decisions. Without clear answers, organizations can’t defend AI-driven outcomes to auditors, regulators, or even internal stakeholders.
Centralisation and Consistency
Industry data suggests that organizations operating under centralized AI governance models are significantly more likely to transition pilots into full production than those relying on fragmented, decentralized approaches. Yet despite this, organizations describe their current state as experimental, manual, or stitched together from one-off solutions.
Compounding this challenge is an over-reliance on custom code and point integrations, which may accelerate early pilots but often become brittle, costly, and difficult to scale, ultimately increasing operational risk and slowing time-to-value.
The result is the same: AI adoption is happening, but without standardization. That lack of consistency makes it difficult to enforce security policies, apply governance controls, or even understand what’s running in production.
Visibility
As organizations across the region accelerate efforts to deploy AI at scale, visibility is emerging as a critical guardrail to ensure autonomous systems operate securely and within policy boundaries. What stands in the way is not budget or executive backing, but the need for trusted infrastructure: continuous visibility into agent behavior, robust monitoring and audit trails, seamless integration with existing workflows, and safeguards that prevent AI systems from weakening established security controls.
Agentic AI is powerful precisely because it can analyze and act. But action without guardrails is unacceptable in production environments. Until security, governance, standardization, and visibility are addressed together, many organizations will remain stuck between pilots and production. These allow them to confidently adopt agentic AI at scale, without sacrificing the controls they’ve spent years building.
