The Impact of GenAI on SaaS Security

The rise of generative AI has ushered in a new era of productivity, automation, and innovation across virtually every industry. But beneath the surface of this technological revolution lies a growing concern: the unintended security risks it brings, particularly within the SaaS ecosystem.

Security practitioners today are facing a dual-edged sword. 

On one hand, GenAI tools like ChatGPT, Claude, Copilot, and others are driving unprecedented efficiency. On the other, they are rapidly expanding the attack surface, often through invisible and unmanaged vectors like shadow AI applications. 

These tools are now routinely interacting with core business data, much of it housed within SaaS applications – introducing new risks around data exposure, compliance, and insider threats.

This article explores how generative AI intersects with SaaS security, what unique risks it introduces, and what proactive steps security leaders can take to address this emerging challenge.

Understanding SaaS Security in 2025

Over the past decade, SaaS has become the backbone of modern enterprise operations. From collaboration tools like Google Workspace and Slack, to CRMs like Salesforce, and development platforms like GitHub, organizations now rely on dozens – if not hundreds – of cloud applications to run their businesses.

SaaS security refers to the strategies and technologies used to secure users, data, and applications across this vast ecosystem. Unlike traditional on-prem environments, SaaS security must account for decentralized access, user-led app adoption, and constantly shifting data flows.

Key pillars of modern SaaS security include:

• Data Access Governance: Ensuring the right users and third-party applications have the right level of access to data.
  
  
• Data Loss Prevention (DLP): Preventing sensitive data from leaving sanctioned applications or reaching unauthorized users.
  
  
• Insider Threat Detection and Response: Monitoring for risky, malicious, or anomalous behavior from users or actors within the organization.
  
  
• Shadow IT and Shadow SaaS: Identifying and managing unsanctioned applications used outside IT’s control.

Security teams are already under so much pressure to maintain visibility and control across this hugely fragmented environment. The now rapid, unregulated adoption of Gen AI tools adds yet another layer of complexity – particularly when those tools start extracting or interacting with SaaS-stored data.

What Is Generative AI?

Generative AI refers to a class of artificial intelligence models capable of producing original content – text, code, images, audio, and more – based on input data. Powered by large language models (LLMs) and other neural networks, these tools learn patterns from massive datasets and generate coherent, context-aware responses.

Examples of popular Gen AI platforms include:

• ChatGPT (OpenAI)
• Claude (Anthropic)
• GitHub Copilot (Microsoft)
• Google Gemini
• Perplexity 

These tools are increasingly embedded in daily workflows. Employees use them to draft emails, generate reports, write code, analyze customer feedback, and more. 

What makes them powerful also makes them dangerous: they require users to input data – and sometimes, that data is highly sensitive.

In many cases, these tools are adopted organically by employees without IT involvement. This creates what we now refer to as shadow AI: unapproved, unmonitored generative AI tools operating outside the bounds of traditional SaaS governance.

What Risks Do Generative AI Introduce to Your SaaS Environment?

While the benefits of generative AI are widely publicized, the risks are only beginning to come into focus. GenAI tools are not inherently malicious per say, but their design and usage patterns introduce a number of unintended security vulnerabilities.

1. Data Exfiltration via Prompts

The most immediate risk is the exposure of sensitive data through prompt inputs. Employees routinely copy and paste internal content (think customer records, source code, financial reports) into GenAI tools to generate summaries, draft communications, or solve technical issues. 

Once submitted, this data may be logged, retained, or even used to further train models, depending on the tool’s terms of service. Without proper controls, this becomes a form of unsanctioned data exfiltration.

2. Lack of Visibility and Control

Unlike managed SaaS platforms, a lot of GenAI tools operate outside of traditional identity providers. These websites and platforms are independent and can be used any time by any employee. 

As a result, security teams have very little visibility into who is using what tool, what data is being shared, or whether GenAI usage complies with company policy. This blind spot makes it nearly impossible to enforce governance or detect anomalies in real time.

3. Data Residency and Retention Concerns

Many Gen AI platforms offer limited transparency around where data is stored, how long it's retained, or whether it’s shared across systems. It’s a well known fact that some large language models retain memory of past conversations.  This, in many cases, is great for productivity. After all, why reintroduce your company context every time you use a tool like ChatGPT, when it could just remember it for you? It’s redundant, boring, and time consuming. Talk about a game changer for saving you time and brain power!

But for organizations governed by regulations like GDPR, HIPAA, or CCPA, this very convenience introduces serious compliance concerns. Without clear boundaries around data locality, retention, and user consent, even well-intentioned use of Gen AI tools can lead to unintentional policy violations or regulatory exposure.

4. Model Training Exposure

Unless explicitly configured otherwise, some GenAI services retain input data for future model training. This means a proprietary business plan, source code snippet, or internal memo could inadvertently become part of a public LLM training corpus, creating long-term exposure of intellectual property (IP).

Do we even need to spell out how problematic that is? If you're working with sensitive information or proprietary assets, the idea that your data could be absorbed into a model – just to make its future responses a little sharper – is more than unsettling. It's a potential IP leak hiding in plain sight.

5. A Different Category of Insider Threats

Most security strategies assume that insider threats are either malicious, negligent, or compromised. However, GenAI introduces the prevalence of a type of insider threat that blurs the lines of what we thought we knew when it comes to categorizing them. GenAI brings on well-intentioned misuse. These types of threats are neither negligent nor compromised due to the risks still being so unknown and unprecedented.

What do we mean by this? Well, employees may unknowingly violate policy by using Gen AI to move faster, get more work done, or take on additional work to help out their team – not realizing the sensitivity or regulatory implications of the data they’re feeding into these models.

How Generative AI Relates to SaaS Security

SaaS and Gen AI are more interconnected than they may first appear. While most security leaders think of ChatGPT or Copilot as isolated tools, the reality is that these platforms are deeply intertwined with core business systems – often in ways that escape traditional monitoring.

Shadow AI Is the New Shadow SaaS: OAuth and Third-Party Integrations

Generative AI tools fall squarely into the category of shadow apps: unsanctioned applications that employees adopt without oversight. But unlike traditional SaaS tools, GenAI often interfaces directly with enterprise data, creating a more potent form of shadow SaaS.

Consider a marketer who exports customer data from Salesforce to paste into a prompt, or an engineer who copies internal logs from a DevOps platform into an AI debugger. These aren’t just isolated data interactions, they’re unsanctioned data pipelines.

Some Gen AI tools now offer integrations with SaaS platforms through OAuth or browser extensions, allowing them to “connect” with your enterprise tools. Without proper vetting, these apps may request broad permissions to read, write, or manage data in connected systems. This creates a new class of third-party risk, often with elevated access levels.

DLP and Security Gaps & Limitations

Traditional DLP solutions and CASBs struggle to detect or block data being transferred to Gen AI tools. Most traffic occurs over encrypted HTTPS and looks like standard web activity. Without deep contextual visibility into user behavior, organizations may never detect sensitive data leaving SaaS environments for GenAI prompts.

SaaS-to-AI Workflows Are Already Happening

The ease of use and availability of Gen AI has enabled a new kind of employee-driven workflow: pulling data from sanctioned SaaS apps, processing it through a GenAI tool, and reinserting the results into business systems. These workflows are not inherently malicious, but they bypass governance entirely – and may violate internal data handling policies or external regulatory requirements.

The Biggest Gen AI Risks to SaaS Security

The collision of generative AI and SaaS has created a set of high-impact risks that are difficult to detect, control, or remediate with traditional tools.

These risks are emerging through actual employee behaviors, third-party integrations, and tool misconfigurations that often escape traditional security monitoring. Below, we explore the most pressing risks and illustrate how they play out in real-world scenarios.

1. Shadow AI Tools and Unauthorized SaaS Use Through OAuth

Employees frequently adopt Gen AI tools independently to streamline tasks – without IT approval or visibility. Some GenAI tools offer deep integrations with popular SaaS apps and request excessive OAuth scopes far beyond what’s required for their functionality. Employees often grant these permissions without reviewing them closely.

• Example: A sales representative connects a GenAI scheduling assistant (think an AI-enhanced version of Calendly) to their corporate Google account. During OAuth setup, the app requests access to their entire Google Calendar and Gmail account. Unbeknownst to the rep, the assistant now has access to confidential meeting notes that include sales pipeline details, deal sizes, and even sensitive customer data – none of which is logged or governed.

2. Internal and External Data Exposure

One of the biggest risks in the GenAI era is the unintentional exposure of sensitive data – either internally to the wrong users or externally through integrations with AI-powered tools. As AI systems increasingly connect with SaaS platforms, maintaining strict access controls becomes more complex, yet more critical than ever.

• Example: Tools like Glean integrate with an organization’s SaaS applications to provide contextualized searching within companies. These tools can inadvertently surface sensitive information to users who shouldn’t have access. It becomes an issue of what user has access to what. You don’t want a junior level analyst being able to search for and find executive board decks or employee salary data. 

3. Prompt Leakage of Sensitive Data

Employees often paste internal documents or data directly into Gen AI prompts to summarize reports, generate content, or troubleshoot code. This can inadvertently expose regulated or proprietary information to third-party platforms.

• Example: An HR manager uses ChatGPT to help rewrite a compensation policy. They paste a draft that includes salary benchmarks, job roles, and equity details. Even if the manager deletes the prompt afterward, the data has already been processed and potentially retained depending on the AI platform’s backend policies. There’s no audit trail, and no clear way to retrieve or delete the data.

4. Malicious or Compromised Extensions

The Gen AI gold rush has led to a surge in AI-powered browser extensions, many of which aren’t vetted by enterprise security teams. These extensions can silently access or scrape SaaS data via browser sessions.

• Example: An engineer installs a Chrome extension that claims to use AI to auto-generate bug reports based on issue tracker activity. The extension reads content from the engineer’s open browser tabs including JIRA tickets and GitHub PRs. The tool turns out to be compromised, and data is silently exfiltrated to an external server.

5. Loss of Regulatory and Policy Compliance

When employees interact with GenAI tools that aren’t compliant with data handling regulations, the organization is exposed to legal and financial risk. Most Gen AI vendors that are publicly accessible today operate on generic terms of service that don’t necessarily meet enterprise compliance standards across industries.

• Example: A healthcare administrator uses an AI writing assistant to generate patient outreach emails. In the process, they input patient appointment details and treatment notes. If the AI tool stores that data in a non-HIPAA-compliant environment, the organization could face regulatory penalties – even though the data exposure happened through seemingly benign usage.

What Security Leaders Can Do Today to Protect Themselves Against GenAI

Generative AI isn’t going away. The challenge for SaaS security leaders isn’t whether to allow it, it’s how to secure it. Here are key actions practitioners can take right now to reduce risk without stifling innovation.

1. Discover and Monitor Shadow AI Usage

Use SaaS security posture management (SSPM), cloud access security brokers (CASBs), or browser security tools to identify where GenAI tools are in use – especially unsanctioned browser extensions, OAuth apps, or third-party integrations. Prioritize visibility before enforcement.

2. Implement Clear AI Usage Policies

Define a company-wide policy that outlines:

• What data is acceptable (and not acceptable) to input into GenAI tools
• Which Gen AI platforms are approved for use
• How employees should evaluate browser extensions or integrations
• Educate employees on risks, especially around data privacy, IP protection, and compliance

3. Vet Enterprise-Grade Gen AI Tools

If your organization wants to embrace GenAI, choose platforms that offer enterprise features: data encryption, no data retention policies, audit logs, and admin controls. Some vendors offer business versions of their tools (like ChatGPT Enterprise) that allow for safer deployment within managed environments.

4. Engage Legal, IT, and HR in Governance

GenAI policy isn’t just a security issue – it’s a business-wide challenge. Collaborate with legal and compliance teams to assess regulatory risks. Work with HR and comms to roll out training. Partner with IT to standardize and monitor GenAI tool usage. Building a cross-functional task force will enable faster, more sustainable governance.

DoControl is All Over GenAI – And You Should Be Too

Generative AI is redefining how businesses operate, unlocking powerful new capabilities while simultaneously introducing complex, often hidden, security risks. As SaaS environments grow more interconnected and data-rich, the addition of GenAI creates a volatile mix: unsanctioned tools, sensitive data, and invisible workflows that bypass traditional controls.

For SaaS security leaders, the message is clear: this is not a passing trend. GenAI is now part of the enterprise fabric. The question isn’t whether it’s being used – because it is. The question is whether you can see it, manage it, and secure it.

At DoControl, we recognize this shift. That’s why we’ve developed GenAI monitoring capabilities that help organizations detect and monitor third-party generative AI tools operating across their SaaS ecosystem. 

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Whether it’s an unsanctioned browser extension accessing Google Drive, or a prompt-based data leak from Salesforce, DoControl gives you the visibility and control you need to mitigate GenAI shadow app risks – before they turn into real incidents.

The future of SaaS security will depend on how well we adapt to the speed of innovation. By taking proactive steps today – discovery, governance, policy, and cross-functional alignment – security leaders can enable safe, responsible AI adoption across the business.