From built-in copilots to third-party chatbots and browser extensions, AI-powered tools are being adopted at breakneck speed, introducing new, and often unseen, Software as a Service (SaaS) security risks. The growing concern for security leaders is how this deep integration of AI into day-to-day workflows contributes to an escalating SaaS AI risk.
The challenge is two-fold. First, AI tools are increasingly able to access, generate, and manipulate data across SaaS environments, often with permissions that go far beyond what’s necessary. Second, the adoption of these tools is often decentralized. Employees or departments may connect AI-powered apps without security team oversight, introducing what is known as shadow AI.
A recent example illustrates the stakes. In March 2023, OpenAI’s ChatGPT experienced a significant data exposure incident due to a bug in the Redis open-source library. This vulnerability allowed certain users to view brief descriptions of other users’ conversations from the chat history sidebar. Additionally, some users were able to see other users’ email addresses and payment information. The issue was traced to a race condition that led to the exposure of active user session data. OpenAI quickly patched the bug and notified those affected, but the incident underscored how even trusted AI-powered SaaS tools can introduce unexpected privacy risks when tightly integrated across user environments.
In this article we’ll explore the SaaS AI risk and actionable ways to detect and reduce the new threats it introduces. We’ll look at how AI increases the SaaS attack surface, how identity-based attacks are evolving, and why visibility, identity threat detection and response (ITDR), and access governance are essential to protecting enterprise environments.
Understanding SaaS AI risk
SaaS AI risks encompass identity exposure, excessive permissions, and the adoption of unauthorized or unvetted tools that leverage AI capabilities. The concern isn’t solely about the applications that enterprises knowingly use but also about the proliferation of shadow AI and its impact on SaaS security.
Unapproved AI-powered tools often enter the environment through browser extensions, personal productivity apps, or department-specific software. Because they bypass formal procurement and security reviews, they can retain persistent OAuth permissions and access sensitive data without accountability.
A 2024 study by SoftwareAG found that more than 50% of knowledge workers globally are using generative AI tools on personal accounts. Even if deployed with good intentions, these tools create blind spots that make it impossible to enforce:
- Identity-based access controls
- Enterprise data handling requirements
- Regulatory compliance policies
However, SaaS AI risk isn’t limited to tools flying under the radar. Even enterprise-approved SaaS apps can pose challenges. AI copilots, document summarizers, and meeting assistants often require persistent access to emails, files, chats, and calendars to deliver useful functionality. In doing so, they dramatically expand the organization’s risk surface.
These tools frequently operate as “black boxes,” with little transparency into how data is processed, stored, or transmitted. Some may route sensitive data back to vendor-controlled environments or use that data to train models, sometimes without clear consent or adequate contractual safeguards. Even vendors that meet baseline security standards may fall short in areas like fine-grained data access control, long-term retention, or incident response readiness.
This lack of transparency and control underscores the importance of implementing robust security measures and continuous monitoring when integrating AI-driven SaaS applications into business operations, especially in areas handling sensitive customer data.
How AI expands the SaaS attack surface
AI dramatically increases SaaS risks across identity, data, and third-party access. Many AI tools require elevated permissions to function effectively. When granted via OAuth, these permissions can remain active even after the user has stopped using the app. This opens the door to misuse if the app is compromised.
Attackers are also using AI to scale identity-based attacks. Automated phishing campaigns can now mimic internal communications with alarming accuracy. Large language models (LLMs) can generate contextually appropriate spear phishing emails, and AI-powered tools can scrape open-source intelligence to craft highly targeted messages.
A notable incident occurred in December 2024 when the U.S. Department of the Treasury was breached through a remote support SaaS platform provided by BeyondTrust. Attackers obtained an API key for the cloud-based service, allowing them to reset passwords and access unclassified documents. This breach highlighted the risks associated with SaaS applications that have deep integrations and elevated privileges within enterprise environments. The incident underscores how vulnerabilities in AI-integrated SaaS tools can be exploited to gain unauthorized access to sensitive data.
To clarify how AI expands the SaaS attack surface, consider the following:
- Persistent access: AI apps often require continuous, always-on access to a wide range of SaaS platforms, such as Google Workspace, Microsoft 365, or Slack. Even after a user stops using the tool, the integration may retain access tokens. If these tokens are not actively revoked, they can be exploited by attackers. These abandoned integrations with persistent OAuth tokens can create hidden backdoors for attackers across enterprise SaaS environments.
- Excessive permissions: Many AI apps request read/write access across multiple data types—emails, calendars, documents, and identity providers—often well beyond what’s needed for their core function. This over-permissioning can dramatically increase the blast radius of an incident. In the case of the CircleCI breach in early 2023, attackers used compromised OAuth tokens with broad scopes to exfiltrate sensitive data from multiple integrated services, affecting downstream customers.
- Opaque data flows: Some AI vendors use data collected from SaaS platforms to train their models or improve algorithms, often without clearly disclosing how and where the data is processed or stored. In 2023, Samsung employees accidentally leaked sensitive chip design information by pasting it into ChatGPT, highlighting the risk of enterprise data being ingested into opaque external systems that lack contractual or regulatory safeguards.
- Interconnected risk: AI tools often integrate with multiple platforms simultaneously, such as CRM, messaging, storage, and identity providers, creating complex, interconnected risk pathways. A single compromised integration can act as a pivot point, allowing attackers to move laterally across systems. In a 2024 advisory, Microsoft warned that attackers were chaining together permissions across cloud services and AI-enabled tools to elevate access and evade detection.
Beyond direct misuse, unmonitored AI integrations can create supply chain threats. For example, an unvetted AI plugin granted access to Slack messages or Salesforce records may exfiltrate sensitive data without ever triggering traditional DLP or EDR tools. The same OAuth trust that enables productivity also enables privilege escalation.
Identity Threat Detection and Response (ITDR)
Traditional security tools are often blind to identity-based SaaS AI risk. That’s where ITDR comes in. ITDR is a security discipline focused on detecting, investigating, and responding to identity threats. As AI applications increasingly rely on user credentials and service accounts, ITDR becomes a crucial layer of defense.
It’s critical to remember that identities aren’t limited to human users. AI tools often operate using non-human identities such as service accounts, API tokens, or automated integrations. These identities can hold extensive privileges and operate at scale and without the same visibility or behavioral patterns as human users. As AI becomes more embedded in SaaS ecosystems, protecting these machine identities is just as important as securing human access.
Wing Security’s ITDR platform provides several advantages:
- AI app discovery: Automated detection of AI-powered SaaS tools, including browser extensions and unsanctioned apps. This helps eliminate blind spots caused by shadow AI and ensures that every integration, authorized or not, is visible and evaluated for risk.
- Identity behavior monitoring: Ongoing analysis of identity activities across SaaS apps to detect anomalies and potential threats. By understanding what “normal” looks like, the system can identify early signs of compromised accounts or insider misuse, enabling faster and more targeted incident response.
- Threat prioritization: Contextual risk scoring based on the sensitivity of data accessed and the privilege level of the identity. This allows security teams to focus their efforts on identities and access paths that, if exploited, could lead to the greatest damage.
- Seamless integrations: Works with existing security tools and SaaS platforms to enhance visibility without introducing alert fatigue. This ensures that ITDR augments the existing security stack rather than overwhelming it, delivering actionable intelligence without noise.
Without ITDR, identity risks stemming from SaaS AI tools often go undetected until it’s too late. Manual reviews and isolated alerts can’t keep pace with the speed and scale of AI-driven threats. Automation is essential, not only to detect identity anomalies in real time, but also to orchestrate timely response actions across complex SaaS ecosystems.
SOC teams need more than static access logs to minimize SaaS risk. They require behavioral insight and automated correlation. By embedding automated intelligence into identity threat workflows, security teams can shift from reactive investigation to proactive containment, significantly reducing the window of exposure.
Best practices to reduce AI security threats in SaaS
While the risks are clear, security teams are not powerless. Defending against SaaS AI risk requires more than policy and vigilance—it demands a coordinated combination of techniques and automated tools that can scale across modern SaaS environments. AI tools operate dynamically, often with broad and persistent access to enterprise data, so security must be equally agile and adaptive.
It’s no longer sufficient to manually audit app permissions or rely solely on endpoint controls. Instead, organizations need to integrate real-time discovery, behavior-based identity threat detection, and automated response into their SaaS security stack. By combining human oversight with automated safeguards, security teams can keep pace with the speed and complexity of AI adoption.
With the right strategy, organizations can mitigate AI security threats in SaaS tools, whether they’re officially sanctioned or introduced through shadow IT. These five key factors provide a structured, proactive approach to managing identity and data risk across an increasingly AI-integrated SaaS environment:
1. Improve visibility across SaaS and AI integrations
You can’t secure what you can’t see. Start by identifying every AI-powered SaaS tool connected to your environment, including those installed independently by employees or departments. These shadow AI tools often go unnoticed by IT and security teams but can still access sensitive data, impersonate users, or move laterally through OAuth integrations.
Visibility should extend beyond app discovery to include each tool’s granted permissions, connected identities, and data access patterns. Continuous monitoring is critical, as tools and permissions can evolve over time. A SaaS security assessment powered by Wing Security’s platform gives SOC teams a unified, real-time view of both authorized and unauthorized AI integrations, enabling faster evaluation and policy enforcement while reducing SaaS AI risk.
2. Tighten access and permissions
Audit OAuth permissions regularly. AI apps often request broad, persistent scopes that remain active even after they’re no longer in use. Over time, these dormant connections can accumulate excessive privileges, creating hidden attack paths. By identifying and revoking unused apps and over-scoped permissions, security teams can reduce the number of available entry points and limit unnecessary exposure.
It’s also essential to enforce least privilege access for every AI integration, especially those touching sensitive systems like email, file storage, and identity providers. This means granting only the minimum level of access required and continuously monitoring for scope changes. Tools that support conditional access and fine-grained control can help ensure AI tools don’t become over-privileged as they evolve or update in the background.
3. Establish clear AI usage policies
Define what types of AI use are permitted, which vendors are approved, and how data can be shared. A clear, enforceable policy helps reduce risk by providing employees with guardrails around acceptable use. It should also specify which types of data, such as customer records, proprietary code, or financial details, must never be shared with AI tools, regardless of vendor assurances.
Policies must evolve with the technology. As AI tools become more deeply embedded in workplace platforms and processes, usage guidelines should be regularly updated to reflect changes in functionality, integration points, and emerging threats. Written policies must be enforced to have an effect, requiring steps such as blocking unauthorized app installs or flagging apps that violate data-sharing rules. Combined, these actions help prevent risky behavior before it leads to a breach.
4. Treat AI vendors as part of your supply chain
Evaluate AI vendors using the same criteria you apply to traditional third-party providers. This includes reviewing their security posture, data handling practices, and history of vulnerabilities. Many AI tools are developed by fast-moving startups, making it essential to vet both their current capabilities and their ability to manage future risks.
Vendor risk doesn’t end at onboarding. SaaS AI platforms regularly introduce new features, expand integrations, or change how they collect and store data. A single update could quietly expand permissions or alter processing methods, creating new exposure points. Continuous monitoring and periodic reassessments help ensure evolving vendor practices don’t introduce unacceptable risk over time.
5. Deploy ITDR to catch what traditional tools miss
Traditional SIEMs and CASBs aren’t built to detect identity misuse across AI-integrated SaaS environments. An ITDR solution that understands SaaS identity behaviors is essential to identifying threats early and mitigating risk. These solutions provide the visibility and context needed to detect subtle behavioral anomalies, such as unusual access times, data downloads, or privilege escalations, that are often missed by static monitoring tools.
Automation is critical to ITDR’s effectiveness. AI-driven SaaS tools operate at scale and speed, and manual investigations simply can’t keep up. Automated ITDR platforms empower SOC teams to correlate identity behavior in real time, prioritize alerts based on context, and take immediate action to contain threats.
By embedding identity intelligence into response workflows, ITDR transforms threat detection from a slow, reactive process into a proactive defense layer purpose-built for the complexity of modern SaaS environments.
The path forward for SaaS AI security
SaaS AI risk is not hypothetical. It’s here, it’s growing, and it demands a proactive response. With AI embedded in everything from email to code review, organizations need to rethink how they govern and monitor identity and data flows.
By expanding visibility, enforcing access controls, setting clear policies, and leveraging ITDR, security teams can counter AI security threats in SaaS environments at machine speed. It’s not enough to block what you don’t understand. To reduce SaaS risk, you need tools and strategies that uncover the unknown and respond in real time. To learn more, explore how Wing Security helps organizations detect and mitigate SaaS identity threats.