Generative artificial intelligence (AI) tools have become workplace staples as employees adopt them to work faster and solve complex problems. Many enter organizations without IT approval, which creates visibility gaps that expose sensitive data to external platforms.
The adoption of unsanctioned AI applications introduces risks related to intellectual property loss and regulatory compliance failures. Detecting unapproved tools has become essential for organizations that want to protect sensitive information while building responsible AI governance frameworks that balance innovation with security.
The growing risk of shadow AI in the enterprise
Some employees turn to publicly available AI tools without involving IT or security teams. Research shows that 78 percent of AI users are bringing their own applications to work, putting company data at risk when sensitive information gets shared with external platforms.
This practice expands beyond traditional shadow IT by creating data governance challenges that security teams struggle to control. Greater visibility into AI usage patterns is necessary before unauthorized applications become embedded in business processes and create difficult dependencies.
Strategies for detecting unsanctioned AI
Three primary strategies give organizations the visibility and control needed to manage unsanctioned AI usage effectively. These approaches work together to create a comprehensive detection framework.
Establish clear AI governance and use policies
Creating and communicating formal guidelines for AI usage establishes acceptable use boundaries and approval processes for new applications. Despite the growing prevalence of AI in workplace environments, 23 percent of employers don’t have a policy related to AI use at work.
Clear documentation should specify which AI applications receive approval, outline acceptable use guidelines, and establish data handling requirements. Effective governance programs include employee education on unsanctioned application risks and regular updates as AI technologies develop.
Employ technical discovery and monitoring tools
Network monitoring solutions, cloud access security brokers (CASB), and endpoint agents help identify traffic patterns and data transfers associated with known AI services. Endpoint detection and response solutions, combined with user behavior analytics, can spot anomalous activity suggesting unauthorized AI usage.
Security teams can use continuous monitoring to detect emerging AI applications and unusual behavior before incidents escalate. This real-time visibility allows teams to intervene quickly and assess whether newly discovered applications should be approved, restricted, or blocked entirely.
Create and maintain an AI application inventory
Building a comprehensive inventory of all AI applications in use allows organizations to classify them by risk level and decide which to sanction or block. In fact, 20 percent of organizations reported a breach due to shadow AI, and not all have policies to detect these unauthorized applications.
Continuously comparing discovered AI services against approved inventories helps identify unsanctioned instances and prioritize risk assessments. Regular reviews also keep classifications current as new applications emerge.
How industry leaders secure their innovations
Organizations that successfully embrace AI innovation understand that productivity gains must balance with strong security and governance practices. Industry leaders combine visibility, policy enforcement, and employee education to identify unauthorized AI usage while enabling responsible adoption across the business.
Darktrace and Direct Federal Credit Union
Direct Federal Credit Union faced the challenge of protecting sensitive member data while maintaining visibility across an expanding digital attack surface. Its limited IT resources and the growing adoption of AI technologies in the workplace aggravated this concern for the group. With 92 percent of security leaders expressing concern about the use of AI agents and their potential security implications, the organization needed to identify threats before they could cause damage.
To strengthen its security posture, the credit union implemented the Darktrace ActiveAI Security Platform. It combines attack surface management, real-time detection, and autonomous response to identify vulnerabilities and automatically contain emerging threats.
The deployment provided comprehensive visibility across the organization’s digital ecosystem and enabled a more proactive approach to cybersecurity. By functioning as a round-the-clock extension of the IT team, Darktrace improved operational efficiency while giving the credit union greater confidence that its systems and members remained protected.
Palo Alto Networks and G42
G42, a fast-growing AI and cloud computing company, grappled with protecting its growing network of locations and sensitive data while maintaining the speed and agility needed to support innovation.
The company partnered with Palo Alto Networks. It deployed a unified security portfolio built around machine learning-powered next-generation firewalls across its offices and remote workforce. The solution established a consistent security architecture that classifies all network traffic and applies security policies based on the application, content, and user rather than traditional network parameters alone.
As a result, G42 strengthened its overall security posture through advanced threat detection and automated investigations while reducing the operational burden on security teams. The unified approach also improved management efficiency and enabled secure remote work, which allowed G42 to focus more on advancing its AI and cloud initiatives.
Zscaler and Repsol
The adoption of generative AI and cloud platforms introduced new security challenges for Repsol, particularly as existing infrastructure struggled to provide adequate visibility into data movement. Without effectively tracking how sensitive information was being accessed, the company faced increased risks of data loss and potential violations of privacy regulations.
Repsol partnered with Zscaler and implemented a zero-trust security architecture designed to deliver greater visibility and control over data interactions. The solution enabled the enforcement of granular security policies and classification-based controls that prevent unauthorized data sharing.
Consequently, Repsol gained the ability to monitor traffic and enforce security policies in real time, which strengthens data protection without disrupting business activities. The deployment also improved employee productivity by providing seamless access to AI tools without compromising security.
Building a proactive AI governance strategy
Technology alone cannot address shadow AI effectively without supporting governance frameworks, continuous monitoring systems, and education programs. As new AI services emerge and threat landscapes shift, organizations must regularly evaluate these applications and adjust their policies to reflect current capabilities and vulnerabilities.
This approach to AI governance enables businesses to embrace innovation confidently while maintaining the controls needed to protect sensitive data and support responsible adoption.
Zac Amos is the Features Editor at ReHack, where he covers business tech, HR, and cybersecurity. He is also a regular contributor at AllBusiness, TalentCulture, and VentureBeat. For more of his work, follow him on X (Twitter) or LinkedIn.
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