Just as IT departments have started to get a handle on shadow IT by setting policies for oversight and permissions on the use of applications, a new challenge has emerged — shadow AI. The rapid adoption of AI-powered workplace tools introduces an unprecedented level of risk. 

While AI promises to supercharge employee productivity, the unsanctioned use of generative AI (GenAI) tools significantly increases the risk of data breaches. Organizations already struggling to get control of their data are now facing even greater challenges, as AI’s self-learning algorithms and data integration capabilities complicate detection and mitigation of data exposure. 

If shadow IT has taught us anything, it’s that governance must evolve in tandem with the adoption of new technologies. To stay ahead, security leaders need measures that not only address current risks but also anticipate AI’s deeper integration into workplaces. 

AI: The new threat vector 

The lure of time savings and improved productivity offered by GenAI tools is one of the most significant game changers since the development of the Internet and World Wide Web. Given all the challenges it can solve, as well as how easy it is to use, employees are jumping on the bandwagon at breakneck speed — often without proper oversight. Compounding this challenge is the integration of AI into operating systems and everyday tools by vendors. 

Unfortunately, this creates an entirely new threat vector. AI tools aggregate and analyze large volumes of sensitive data, from financial information, including transaction records, budgets, and strategic plans, to source code and intellectual property (IP). This data, used to train these models, provides another window into an organization’s intellectual property, the likes of which we have never seen before, and poses a significant risk of data exfiltration.

Cybercriminals may leverage this to gather important information on an individual or organization and, if the AI platform itself is attacked and compromised, that data could be used to feed other AI tools, compounding the damage. Employees uploading company data into GenAI platforms must understand that this information is also used to train the tool, effectively giving up any ownership rights and exposing it to potential misuse.

Establishing AI policies 

To mitigate the risks of shadow AI, developing comprehensive AI governance policies should be the first step for every security leader. These policies must outline how, when, and by whom sanctioned AI tools can be used. They should also include extensive guidelines for approving AI applications, detailed protocols for handling sensitive data, and a schedule for regular compliance audits.

Before implementing any AI tool, security teams must strictly evaluate whether it meets security standards and compliance requirements. This process should involve a detailed review of the AI tool’s data processing, storage, and sharing capabilities to safeguard sensitive information at every stage. 

It’s also crucial to make the distinction between consumer-grade GenAI and secure enterprise solutions when evaluating tools and the risks they pose for data security. Typically, enterprise systems will have enhanced security features and guardrails, such as encryption and access controls, to protect sensitive data processed by AI systems.

Additionally, creating and maintaining a whitelist of approved AI tools — vetted for security and compliance — is a practical way to ensure sensitive work is conducted only through sanctioned applications. Most importantly, organizations should conduct regular reviews and update this list to keep pace with evolving AI technologies and ensure policies stay relevant and effective.

AI access management and monitoring 

Beyond policy enforcement, technical measures are crucial in mitigating the risks associated with shadow AI. Implementing AI usage monitoring tools, such as network traffic analysis systems and user behavior analytics platforms, plays an important role by identifying unauthorized tools and flagging potential policy breaches. 

Managing access to AI tools through identity and access management (IAM) solutions ensures that only authorized personnel can use AI applications, reducing the risks of data exposure. Additionally, IAM solutions serve as a secure intermediary between employees and AI tools, enforcing security policies, monitoring data exchanges, and blocking the unauthorized use of AI applications.

Any sensitive corporate data that is exposed outside the organization poses a risk, and organizations should also ensure that they have security measures in place to prevent sensitive data from leaving a device during a cyberattack. This is particularly important in light of attackers increasingly leveraging exfiltrated data for double — or even triple — extortion. To mitigate this risk, monitoring and controlling data transfers is essential to ensure sensitive data remains secure and does not leave the endpoint. 

Regulation and compliance 

Anticipated regulatory frameworks will also shape GenAI’s role in the workplace, as governing bodies increasingly focus on its data privacy and security implications. 

For example, the European Union’s General Data Protection Regulation (GDPR) already imposes stringent requirements on data processing, and the upcoming EU AI Act will further regulate the use of AI technologies. 

The National Institute of Standards and Technology (NIST) has released its AI risk management framework, providing voluntary guidelines to help organizations manage AI risks and promote trustworthy AI usage. The Securities and Exchange Commission (SEC) has also proposed regulations to manage conflicts of interest posed by AI for broker-dealers and investment advisers, while the California Privacy Protection Agency (CPPA) has drafted regulations focusing on automated decision-making technologies. 

With AI adoption, development, and regulations evolving rapidly, companies must proactively adapt their AI strategies to ensure compliance and security. Shadow AI may be the latest nightmare for security leaders, but with comprehensive policies, proactive strategies, and vigilant monitoring, its risks can be effectively managed, thereby protecting sensitive data from both inadvertent exposure and targeted exfiltration efforts.