Ramy Muhammad Ahmad, Senior Director, Solutions Engineering, IMETA, Exabeam warns that AI adoption is expanding insider threats across MEA, urging organisations to monitor both humans and AI agents, strengthen governance, and use behavioural analytics to detect risks at machine speed.
Rising AI adoption in the Middle East and Africa (MEA) is introducing greater insider risk to the region. Insider threats, whether intentional or accidental, are one of the most dangerous risks to an organization. This challenge is intensifying as organizations integrate AI-powered technologies, exposing them to risks ranging from credential compromise to AI misuse.
Currently, nearly 90% of cybersecurity professionals in the Middle East believe leadership significantly underestimates insider risk, according to Exabeam research. While external cyberthreats are widely recognized and prioritized, the same level of understanding and urgency must be applied to insider threats. This gap is becoming more dangerous as the definition of an “insider” expands to encompass not only human-based risk, but the AI tools and platforms integrated to support business functions.
At the same time, enterprise environments are distributed across SaaS applications, cloud infrastructure, identity systems, APIs, and AI-driven platforms at a scale and tempo that exceeds human-centered workflows.
As the insider landscape continues to grow, it is more important than ever that business leaders take notice and proactively address this expanding attack surface. This means building awareness of the different types of insider threats and the risks they pose to the entire organization.
Insiders in the AI Era
Across MEA, insider threats are exposing gaps in visibility within user activity. The primary challenge is that the insider, whether malicious, compromised or negligent, is using legitimate credentials within their actions. To legacy security tools that rely on static rules, their activity looks normal meaning behavior isn’t flagged as suspicious.
This problem is magnified for non-human insiders, such as custom AI agents as activity is often programmatic and high-volume. The way these agents work makes it nearly impossible for human analysts or rule-based systems to distinguish between normal operations and a compromised state without a behavioral baseline.
Enterprises now operate with both human employees and digital workers, as AI agents act across systems, data, and APIs, increasing operational velocity and expanding insider risk.
With AI tools becoming more integrated into everyday business functions, increased insider risk is being introduced to organizations through:
- Shadow AI: The rise of Shadow AI, where employees use unapproved AI tools like GenAI chatbots, creates hidden risks for organizations. Such unsanctioned usage can lead to accidental data exposures and activities that evade IT monitoring. This lack of oversight puts organizations at risk of regulatory violations, intellectual property theft, and untraceable internal actions.
- Sophisticated Deepfakes: Employees are increasingly targeted with sophisticated attacks powered by GenAI. Deepfakes, forged documents, and highly realistic phishing messages can convincingly impersonate executives or trusted partners. This can lead to fund transfers, compromised credentials, and clicks on malicious links that bypass conventional security defenses.
- Unmonitored AI Agents: AI agents represent both unprecedented productivity and, left unchecked, unprecedented risk. When misconfigured, compromised, or acting unexpectedly, AI agents can function like inadvertent insiders, widening the organization’s attack surface and creating new security vulnerabilities.
The introduction of AI into enterprise environments is already reshaping the threat landscape. As AI technologies continue to evolve and adoption becomes more widespread, security leaders cannot afford to take a passive stance.
To address this, organizations are beginning to explore the Accelerated Security Operations model where human insight meets machine speed to create a continuously adaptive, policy-driven defense.
Securing the Insider Attack Surface
Insider risk today is no longer limited to people alone. It also includes the AI tools that support daily operations. These same technologies can be exploited by malicious actors or unintentionally misused by employees, placing security teams in the position of fighting threats with the very tools being used against them.
The operating assumptions behind the security operations center (SOC) were formed in a different era. Data was relatively centralized. Attackers moved at human speed. Today, scaling the existing model through additional tooling or workflow automation does not close that gap. These pressures are converging into structural forces that redefine how security operations must function.
As a result, organizations across MEA are boosting their investment in AI-driven security analytics to detect threats before they escalate. With these tools, they gain effective threat detection, investigation, and response (TDIR) by securing against modern insider threats, whether they originate from human users, entities, or AI agents.
Beyond this, organizations are also having to think about how they can reduce risk through proactive controls, security awareness training, and effective governance:
- Implementing Preventative Controls. Preventive controls are the foundation of any security program. These include identity and access management (IAM), privileged access management (PAM), and data loss prevention (DLP) to reduce exposure, limit privileges, and protect critical data from misuse. Another aspect of these preventative controls is security awareness training. This consists of teaching employees how to safely use AI tools, spot impersonation and phishing attempts, and avoid risky behaviors.
- Automating Behavioral Detection. AI agents are not external tools. They are non-human insiders operating inside enterprise environments. As digital workers scale, that behavioral model must extend to agents. Agent Behavior Analytics (ABA) provides a centralized platform for monitoring the activity of AI agents and automated entities. This equips analysts with the context and forensic timelines required to efficiently analyze suspicious activity.
- Deploying Effective Governance: It is essential that organizations across MEA implement strong governance frameworks to ensure responsible use of AI tools. Policies that combat intentional or unintentional AI misuse should include model training, data access control, and system oversight. This reduces the risks of insider threats occurring from overlooked AI systems.
Turning the Tides with AI
Across MEA, AI continues to reshape the threat landscape by increasing both the speed and complexity of attacks whilst providing powerful defense tools. To stay ahead, organizations can no longer rely on traditional insider threat models and must rethink how risk is identified and managed. Today’s brittle automation must be replaced with an adaptive, policy-driven model that operates at machine speed while preserving human judgment. The rise of agentic systems, distributed enterprise data, and the escalating pace of threats make this evolution both necessary and inevitable.
Doing so requires organizations to extend monitoring to cover not just employees, but also AI entities and autonomous systems. Those that unify behavioral analytics with strong governance and proactive controls will be best prepared to manage insider risk in an AI-enabled environment.