Generative artificial intelligence (AI) tools have become a staple in the workplace, with employees deploying them to work faster and solve complex problems. Many enter organizations without IT approval, creating visibility gaps and exposing sensitive data to external platforms.
Deploying unapproved AI applications creates risks related to loss of intellectual property and failure to comply with regulations. Detecting unauthorized tools has become essential for organizations looking to protect sensitive information while building a responsible AI governance framework that balances innovation and security.
The growing risk of shadow AI in the enterprise
Some employees leverage publicly available AI tools without involving IT or security teams. Research shows that 78% of AI users deploy their own applications, putting corporate data at risk when sensitive information is shared with external platforms.
This practice extends beyond traditional shadow IT by creating data governance challenges that security teams struggle to control. Greater visibility into AI usage patterns is needed before rogue applications embed themselves into business processes and create difficult dependencies.
Strategies for detecting unauthorized AI
Three key strategies can give organizations the visibility and control they need to effectively manage unauthorized use of AI. These approaches work together to create a comprehensive detection framework.
Establish clear AI governance and usage policies
Creating and communicating formal guidelines for AI use establishes acceptable use boundaries and approval processes for new applications. Despite the increasing prevalence of AI in the workplace, 23% of employers do not have a policy regarding the use of AI in the workplace.
Clear documentation should specify which AI applications receive approval, outline acceptable usage guidelines, and establish data processing requirements. An effective governance program includes educating employees about the risks of unauthorized applications and regular updates as AI technology evolves.
Employ technical discovery and monitoring tools
Network monitoring solutions, cloud access security brokers (CASBs), and endpoint agents can help identify traffic patterns and data transfers related to known AI services. Endpoint detection and response solutions, combined with user behavior analytics, can identify anomalous activity that suggests AI abuse.
Security teams can use continuous monitoring to detect new AI applications or anomalous behavior before an incident escalates. This real-time visibility allows teams to quickly intervene and assess whether newly discovered applications should be approved, restricted, or completely blocked.
Create and maintain an AI application inventory
By building a comprehensive inventory of all AI applications in use, organizations can categorize them by risk level and decide which to sanction or block. In fact, 20% of organizations report breaches due to shadow AI, but not all organizations have policies to detect these malicious applications.
Continuously comparing discovered AI services to authorized inventory allows you to identify unauthorized instances and prioritize risk assessment. Regular reviews also keep the classification up to date as new applications emerge.
How industry leaders secure innovation
Organizations that successfully implement AI innovation understand the need to balance increased productivity with strong security and governance practices. Industry leaders are combining visibility, policy enforcement, and employee education to identify fraudulent AI uses and enable responsible adoption across the business.
Darktrace and Direct Federal Credit Union
Direct Federation Credit Union faced the challenge of protecting sensitive member data while maintaining visibility across a growing digital attack surface. Limited IT resources and the increasing adoption of AI technology in the workplace further exacerbated this concern for the group. With 92% of security leaders expressing concern about the use of AI agents and their potential security impact, organizations needed to identify threats before they cause damage.
To strengthen its security posture, the credit union deployed the Darktrace ActiveAI security platform. Combining attack surface management, real-time detection, and autonomous response to identify vulnerabilities and automatically contain emerging threats.
This implementation provides comprehensive visibility across the organization’s digital ecosystem and enables a more proactive approach to cybersecurity. By acting as an extension of the IT team around the clock, Darktrace improved operational efficiency and gave the credit union confidence that its systems and members remained protected.
Palo Alto Networks and G42
G42, a fast-growing AI and cloud computing company, is committed to protecting its expanding network of locations and sensitive data while maintaining the speed and agility needed to support innovation.
The company has partnered with Palo Alto Networks. The company has deployed an integrated security portfolio built around a next-generation firewall powered by machine learning across its offices and remote workforce. The solution established a consistent security architecture that classifies all network traffic and enforces security policies based on applications, content, and users, not just traditional network parameters.
As a result, G42 strengthened its overall security posture through advanced threat detection and automated investigation while reducing the operational burden on security teams. The unified approach also improves management efficiency, enables secure remote work, and allows G42 to further focus on advancing its AI and cloud initiatives.
Zscaler and Repsol
The introduction of generative AI and cloud platforms has created new security challenges for Repsol, especially as existing infrastructure struggles to provide adequate visibility into data movement. Without effectively tracking how sensitive information was accessed, the company faced increased risk of data loss and potential violations of privacy regulations.
Repsol partnered with Zscaler to implement a Zero Trust security architecture designed to improve visibility and control of data interactions. With this solution, we can now apply granular security policies and classification-based controls to prevent unauthorized data sharing.
As a result, Repsol gained the ability to monitor traffic and enforce security policies in real time, increasing data protection without disrupting business operations. This implementation also increased employee productivity by providing seamless access to AI tools without compromising security.
Build a proactive AI governance strategy
Technology alone cannot effectively address shadow AI without supporting governance frameworks, continuous monitoring systems, and education programs. As new AI services emerge and the threat landscape changes, organizations should regularly evaluate these applications and adjust policies to reflect current capabilities and vulnerabilities.
This approach to AI governance allows businesses to embrace innovation with confidence while maintaining the controls needed to protect sensitive data and support responsible adoption.
Zack Amos She is ReHack’s features editor, covering business technology, human resources, and cybersecurity. He is also a regular contributor to AllBusiness, TalentCulture, and VentureBeat. To learn more about his work, follow him on X (Twitter) or LinkedIn.
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Featured image: Egor Komarov on Unsplash
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