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Bridging the Security Gap by Addressing Visibility Challenges with Generative AI Usage

BY Jeff Schwartzentruber

June 27, 2024 | 7 MINS READ

Managed Detection and Response

Cybersecurity Strategy

Generative AI

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Generative AI (GenAI) technologies have become an integral part of business operations, transforming the way they operate. Companies are leveraging these technologies to streamline processes, enhance customer interactions, drive innovation, and do more with less.

According to a 2024 McKinsey Global Survey, 65% of respondents shared that their organizations are regularly using GenAI.

However, as the reliance on these tools increase, a significant challenge has emerged: the lack of visibility into how employees are using these applications. Without comprehensive oversight, many organizations are struggling to govern GenAI usage effectively, leading to potential security risks:

With our recently announced launch of eSentire MDR for GenAI, we’re helping organizations take steps to begin addressing these risks. By enabling security leaders with visibility into AI usage patterns, organizations can focus on policy adherence and risk reduction across their environments.

In this blog, we explore the risks associated with unmonitored GenAI usage, the impact on cybersecurity, and why you need comprehensive visibility over your employees’ Generative AI usage. By understanding and addressing these visibility challenges, you’ll be better prepared to protect your organization and ensure responsible AI usage.

Potential Cyber Risks from Unmonitored GenAI Usage

There’s no doubt that the benefits of using GenAI tools are substantial – they enable resource-limited teams to automate repetitive tasks, drive efficiency, and focus on more strategic initiatives. However, they also introduce notable cybersecurity risks:

Oversharing Sensitive Data and Compromising Data Security

Generative AI tools are designed to facilitate easy data sharing and processing, but this convenience comes with increased risks of oversharing. Employees may unknowingly share sensitive customer data property with AI tools, which can lead to unintended data exposure.

For example, entering confidential business strategies or sensitive customer data into an AI application could result in this data being stored, processed, and potentially accessed by unauthorized parties. This not only jeopardizes the security of critical information but also exposes the organization to legal and financial liabilities.

Loss of Trade Secrets

Trade secrets, which include proprietary formulas, business strategies, and other confidential information, are crucial to maintain a competitive edge. When employees use GenAI tools without oversight, they might unknowingly input sensitive data into these applications. Complicating matters further, many GenAI platforms have terms of service that grant the AI vendor rights to use the input data to improve their models, which can result in unintended exposure of trade secrets.

Loss of Privilege

Depending on the industry you’re in, there can be significant impact associated with loss of privilege extended between attorneys and clients. For instance, if legal professionals use GenAI to draft or review confidential legal documents, the content entered into the GenAI platform could be accessed by the vendor, potentially waiving the privilege that protects these communications. This loss of privilege can have severe legal implications, compromising the confidentiality of sensitive legal strategies and communications.

Contractual Violations

Many business contracts include strict confidentiality clauses that prohibit sharing third-party data with unauthorized entities. When employees use GenAI applications without proper monitoring, they might inadvertently share data covered under these agreements.

This can lead to breaches of contract, resulting in legal disputes, financial penalties, and damage to business relationships. Additionally, sharing data with GenAI vendors without evaluating the vendor's data security practices can increase the risk of data breaches and compliance violations.

Ethical and Legal Concerns

Without clear ethical guidelines and monitoring, Generative AI applications can produce content that infringes on copyright laws, which can lead to costly legal battles and damage your company's reputation. On the other hand, AI-generated content can also perpetuate biases, stereotypes, and discrimination, leading to ethical scrutiny and erosion of public trust.

These concerns highlight the importance of establishing robust monitoring and governance frameworks to guide how your employees use Generative AI tools. Implementing comprehensive oversight mechanisms can help organizations manage these risks, protect sensitive information, and ensure compliance with legal and contractual obligations.

Why Maintaining Visibility is Crucial for GenAI Usage

Many organizations currently lack effective systems to monitor the usage of Generative AI applications by employees. This absence of oversight creates significant blindspots, leaving security leaders in the dark about how these tools are being used within their networks.

Blindspot #1: Security Vulnerabilities

Without visibility into employees’ AI interactions, you cannot detect unauthorized or inappropriate use of these tools. This lack of awareness can allow malicious activities to go unnoticed, increasing the risk of data breaches and other security incidents.

Blindspot #2: Policy Limitations

Effective policy crafting requires detailed insights into AI interactions, which can only be achieved through comprehensive monitoring. Lack of visibility also impedes your ability to create and enforce effective risk management and governance policies around acceptable GenAI usage. Since you can’t develop targeted guidelines or implement controls effectively, it’s challenging to address specific risks and ensure compliance with regulatory requirements.

Blindspot #3: Sensitive Data Leaks

Consider a scenario where an employee uses a GenAI tool to draft a contract, inadvertently sharing confidential customer details with the AI vendor, or using an unapproved AI application to handle sensitive customer information, leading to a significant data leak. Without monitoring, the organization remains unaware of this data exposure until it results in a legal dispute or data breach.

Without dedicated monitoring systems, companies cannot track interactions with AI applications, making it challenging to enforce policies and ensure compliance with internal guidelines. By implementing comprehensive monitoring systems, organizations can mitigate risks, enforce policies effectively, and ensure responsible AI usage.

Why Comprehensive Visibility and Risk Monitoring are Essential to Secure Against Unauthorized GenAI Usage

Comprehensive visibility into how your employees use the various Generative AI tools helps manage the cyber risks outlined above by enabling proactive security measures, enhancing governance, and providing peace of mind to security leaders.

Reduce Cyber Risks

Since visibility enables you to detect unauthorized data sharing, inappropriate use of AI applications, and protect against data breaches, it’s crucial for mitigating risks associated with data security and reducing organizational risk. In addition, having complete visibility enables you to proactively protect sensitive data and ensure compliance with regulatory requirements. This reduces the likelihood of data leaks, intellectual property theft, and other security breaches that can have severe legal and financial repercussions.

Enhance Governance

With a clear understanding of how AI tools are being used, you can craft targeted policies that address specific risks and usage patterns. You can also develop guidelines that promote ethical and responsible AI usage, ensuring that your employees adhere to best practices and legal standards. Plus, by monitoring how your employees use GenAI tools, you’ll have the data needed to refine and adjust policies over time, making governance a dynamic and responsive process.

Peace of Mind

There’s no doubt that with total visibility comes peace of mind. Since you can continuously monitor and manage GenAI-related risks, you can quickly identify and resolve potential security concerns and minimize their impact on business operations.

In doing so, you can now confidently leverage the benefits of Generative AI without compromising on cybersecurity. This confidence extends to stakeholders, customers, and regulators, who can trust that your organization is committed to maintaining high standards of data security and adherence to regulatory frameworks.

How eSentire MDR for GenAI Visibility Protects Against Unauthorized GenAI Usage

eSentire MDR for GenAI Visibility provides metric-driven, comprehensive visibility into your organization’s GenAI application usage to determine potential risks. This visibility into GenAI usage offers early warnings of employees sharing sensitive information or noncompliance to corporate policies, helping monitor risks before they become business critical events.

Benefits of implementing eSentire MDR for GenAI Visibility include:

While Generative AI tools offer significant benefits, including enhanced productivity and innovation, they also introduce considerable security risks. The lack of visibility into how employees use these applications can lead to data breaches, intellectual property loss, and compliance violations.

By implementing robust monitoring solutions, you can ensure responsible AI usage, protect sensitive information, and maintain compliance with regulatory requirements. This proactive approach not only safeguards the organization but also supports its strategic objectives by enabling secure and efficient AI integration.

To learn how eSentire MDR for GenAI Visibility helps you secure your organization against unauthorized use of Generative AI tools, connect with an eSentire security specialist now.

Jeff Schwartzentruber
Jeff Schwartzentruber Senior Machine Learning Scientist

Dr. Jeff Schwartzentruber is a Sr. Machine Learning Scientist at eSentire. His primary academic and industry research has been concentrated in solving problems at the intersection of cybersecurity and machine learning (ML). For 10+ years, Dr. Schwartzentruber has been involved in applying ML for threat detection and security analytics for several large Canadian financial institutions, federal public sector organizations, and SMEs. In addition to his private sector work, Dr. Schwartzentruber is also an Adjunct Faculty at Dalhousie University in the Department of Computer Science, a Special Graduate Faculty member with the School of Computer Science at the University of Guelph, and the Sr. Advisor of AI at the Rogers Cybersecure Catalysts.

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