Shadow AI agents: What CISOs need to know

Shadow IT thrives in the cloud era, where the on-demand nature of cloud environments enables employees to access a host of tools, both business-sanctioned and unapproved.

When time is of the essence, shadow IT holds a strong appeal for staff to use tools that improve their output and efficiency. Users can deploy new technology faster without the holdups in the normal approval process. Of course, the downside to shadow IT is that unsanctioned, under-secured technology can put the organization and its intellectual property at risk.

Over time, IT departments have created shadow IT policies to better limit fallout from unauthorized technology access. But the rise of agentic AI poses new shadow IT challenges as business units look for quick ways to tap into the technology, often bypassing centralized IT approvals.

Adding agentic capabilities to the mix -- where autonomous or semi-autonomous chatbots, tools, coding assistants and workflow automation agents have a level of self-governance that previous technology lacked -- quickly turns shadow AI problematic.

The prevalence of AI raises serious concerns about how to address the security of shadow AI. Akamai's "Enterprise AI Usage Risk Report 2026" found that nearly half of all AI conversations are conducted through personal rather than corporate accounts. This can quickly translate into serious -- and costly -- problems. In fact, shadow AI-driven breaches have more than doubled from 20% to 43% in one year, according to IBM's "Cost of a Data Breach Report 2026."

Benefits and risks of shadow AI agents

There is inherent risk in any unmanaged -- and thus under-secured -- entity that has access to corporate resources, such as data exfiltration, compliance issues and model poisoning.   The introduction of shadow AI into enterprises raises serious red flags for cybersecurity teams.

It is critical for CISOs to recognize the benefits of shadow AI and understand why employees might circumvent standard procedures to access the technology faster. Agentic AI promises a host of benefits, including a high degree of autonomy, automating repetitive tasks and quickly adapting to environmental changes. The technology can also handle specific dynamic tasks -- such as threat detection and supply chain changes -- more efficiently than methods that require more manual intervention.

End users gravitate toward agentic AI to support a range of actions, including the following:

• Pasting the company's source code into public AI tools to write functions.
• Applying data analysis to customer data, financial spreadsheets or other documents to create summaries or charts.
• Writing client emails, generating marketing strategies and creating press releases.
• Translating legal or other corporate documents.

CISOs also need to recognize how shadow AI is different from traditional shadow IT. While conventional, non-agentic shadow IT is only transmitted, stored or edited when a user interacts with it, shadow agentic AI possesses a level of autonomy that does not require human intervention.

Shadow agentic AI also has advanced processing capabilities, relying on machine learning to read, translate, edit and assess text, images and code. Traditional shadow IT lacks any understanding of the content it stores and moves. Conventional AI also lacks decision-making capabilities, while agentic AI can make conclusions without the express direction of an end user.

CISOs need to understand the risks inherent in deploying shadow AI agents without adequate security measures. Shadow AI agents possess a level of autonomy that poses serious risks of data leaks and privacy loss. Employees can knowingly or unknowingly expose the company's intellectual property, content or source code. Shadow AI might violate regulatory requirements, such as GDPR and HIPAA, and security professionals unable to track that data might be unable to meet auditing requirements.

Some elements associated with the use of shadow AI agents, such as browser extensions, fall short on security controls. Adversaries can also exploit vulnerabilities -- such as weak integration points -- to access network resources. Some tools used in shadow AI can hallucinate or produce inaccurate output that appears factual, undermining decision integrity.

All of this puts enterprises at great peril of loss or misinformation that results in direct financial costs through impeded productivity, legal costs and competitive losses, as well as long-term reputational damage.

Defending against shadow AI vulnerabilities

The first step to laying the groundwork for a more productive and secure environment is to understand agentic AI's benefits and why employees turn to shadow IT to access them. This starts with knowledge gained through discovery and accurate inventory. IT teams need to track traffic and look for unsanctioned AI tools. They must determine which departments are deploying AI and what confidential or high-value data AI might process, store and transmit.

Security practitioners should educate employees on potential risks associated with shadow AI agents. Amnesty programs can let end users come forward about shadow AI use without fear of repercussions.

CISOs need to consider ways to improve the approval process to create faster pathways to access and offer sanctioned AI alternatives.

All organizations should have the technical safeguards in place that extend to shadow AI, such as data loss prevention tools that prevent high-value data from interacting with public AI models. They should also enforce access control through policies and procedures that restrict potentially dangerous connections. Security teams should monitor usage for any unusual activity.

As with all effective security controls, a solid defense starts with communication, which encompasses strong staff training and end-user education. CISOs need to provide clear guidance on AI usage and shadow IT to both IT and end users. All lines of business -- from marketing and procurement to IT -- must work together to share in responsible and sanctioned AI use or risk exposing the enterprise to shadow AI vulnerabilities.

Amy Larsen DeCarlo has covered the IT industry for more than 30 years, as a journalist, editor and analyst. As a principal analyst at GlobalData, she covers managed security and cloud services.