Mitigating GenAI Risks in SaaS Applications

Originally published by Valence Security and Forbes.

Written by Jason Silberman.

Artificial Intelligence (AI) tools have revolutionized the business landscape, offering unprecedented automation, efficiency, and innovation. Among these, Generative AI (GenAI) has gained particular traction for its ability to produce creative content, write code, and assist in decision-making. When integrated into SaaS applications, these tools can transform business operations. However, with this rapid adoption comes significant generative AI security risks, especially as organizations struggle to manage and secure these tools effectively.

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The Double-Edged Sword of GenAI in SaaS

The widespread integration of GenAI tools with popular SaaS platforms like Microsoft 365, Google Workspace, and Salesforce presents a complex security challenge. According to the 2024 State of SaaS Security Report, 50% of security leaders have flagged GenAI governance as a critical SaaS security concern. The promise of automation and productivity through GenAI must be balanced against the significant risk these tools introduce.

While platforms like OpenAI’s ChatGPT offer immense utility, they often require extensive access to sensitive data within SaaS environments to function effectively. Without stringent oversight, this opens the door to potential data breaches, privacy violations, and unsanctioned access. That oversight, however, is not always so simple when it comes to SaaS security. One of the key challenges in managing the risks posed by SaaS-to-SaaS integrations, including GenAI tools, is the distributed ownership of SaaS applications across different business units. For instance, Salesforce may be managed by sales operations, outside of the direct control of IT and security teams. This decentralized ownership limits the visibility security teams have over these applications, making it difficult to track, assess, and remediate integration risks effectively.

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What Is Shadow AI in SaaS?

Shadow AI refers to the adoption and use of AI tools by employees without formal IT or security approval. This unsanctioned use of GenAI tools within SaaS applications can create blind spots for security teams, leading to unmonitored data access and the risk of exposing sensitive information. With the rapid growth of these tools, especially free trials or low-barrier integrations, the presence of Shadow AI in SaaS environments is on the rise. Security teams must address this growing risk to prevent data leakage and maintain control over the organization’s SaaS security posture.

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Top 5 Security Concerns with GenAI in SaaS

1. Unsanctioned Use (Shadow AI): A recent survey by The Conference Board found that 56% of US employees already use GenAI tools at work, often without IT or security oversight, creating visibility and control gaps. Free trials and readily available integrations can entice business users to adopt GenAI tools without proper oversight.
2. Wide Access to Data: GenAI tools often require access to a broad range of data within SaaS environments, increasing the risk of exposure. According to the 2024 State of SaaS Security Report, 33% of SaaS integrations are granted sensitive data access or privileges to the core SaaS application. This broad and often unrestricted access raises the potential for data breaches and unauthorized access to sensitive information within platforms like Zoom recordings, instant messaging in Slack, or sales pipeline and customer data in Salesforce.
3. Privacy Violations: Many GenAI tools collect user data for training purposes. Organizations could inadvertently expose their data or violate regulations like GDPR or CCPA if they don't carefully scrutinize the privacy policies and data usage terms of GenAI tools. More concerning, GenAI models can inadvertently leak sensitive information during outputs, compromising confidentiality..
4. Lack of Transparency: Understanding how GenAI tools operate and make decisions can be challenging. This lack of transparency makes it difficult for security teams to identify and mitigate potential security risks.
5. Business User Risks: While leveraging GenAI's potential, business users might overlook security considerations. This can happen during integration with core SaaS applications. Critical security decisions, like the level of data access granted to the GenAI tool or reporting the integration to IT, can be missed during this process, increasing security risks.

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Governing GenAI in SaaS: Key Strategies

To address these risks, security teams must take proactive steps:

• Create a GenAI Security Policy: Define clear policies for generative AI security and adoption, including guidelines for data access, tool approval, and employee training.
• Centralized Visibility: Use a SaaS Security Posture Management (SSPM) platform to gain visibility into all GenAI integrations and manage their data access.
• Enforce Least Privilege Access: Apply the principle of least privilege to limit the data that GenAI tools can access.
• User Education: Educate employees on the risks of unsanctioned GenAI tools and best practices for safe integration.
• Continuous Monitoring: Stay updated on emerging GenAI trends and regularly assess the security of your GenAI-integrated SaaS environment.

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What Is The Future of Generative AI in Cybersecurity?

Of course, discussion of GenAI adoption and governance extends beyond SaaS applications. As Generative AI continues to evolve, so too will its role in cybersecurity. While the automation capabilities of GenAI tools offer promising opportunities for threat detection and response, they also open new attack vectors for cybercriminals. The challenge will lie in balancing innovation with generative AI security measures to ensure these tools are leveraged safely. AI-driven attacks, such as phishing schemes generated by GenAI, could become more sophisticated, making it essential for security teams to stay ahead of emerging threats. Ensuring secure and compliant usage of GenAI tools will be a central focus as we move into the future of cybersecurity.