Over the past year, AI co-pilots and agents have quietly permeated the SaaS applications that businesses use every day. Tools like Zoom, Slack, Microsoft 365, Salesforce, and ServiceNow now come with built-in AI assistants or agent-like features. Virtually all major SaaS providers have rushed to integrate AI into their offerings.
The result is an explosion of AI capabilities in the SaaS stack, a phenomenon of The sprawl of AI where AI tools proliferate without centralized oversight. For security teams, this represents a change. As their use increases, these AI co-pilots are changing the way data flows through SaaS. An AI agent can connect multiple applications and automate tasks between them, creating new integration paths on the fly.
An AI meeting assistant can automatically pull documents from SharePoint to summarize them in an email, or a sales AI can cross-reference CRM data with financial records in real time. These AI data connections form complex and dynamic paths that traditional static application models never had.
When AI fits in – Why traditional governance is collapsing
This change exposed a fundamental weakness in the security and governance of existing SaaS. Traditional controls assumed stable user roles, fixed application interfaces, and human-paced changes. However, AI agents break these assumptions. They operate at machine speed, traverse multiple systems, and often have higher privileges than usual to perform their work. Their activity tends to blend in with normal user logs and generic API traffic, making it difficult to distinguish between the actions of an AI and those of a person.
Take Microsoft 365 Copilot for example: when this AI retrieves documents that a given user would not normally see, it leaves little or no trace in standard audit logs. A security administrator may see a trusted service account accessing files and not realize that Copilot is retrieving confidential data on someone’s behalf. Likewise, if an attacker hijacks an AI agent’s token or account, they can abuse it discreetly.
Additionally, AI identities don’t behave at all like human users. They don’t fit neatly into existing IAM roles and often require very broad data access to function (much more than a single user would need). Traditional data loss prevention tools struggle because once an AI has broad read access, it can potentially aggregate and expose data in ways that no simple rule could capture.
Permission creep is another challenge. In a static world, you can review onboarding access once per quarter. But AI integrations can change capabilities or accrue access quickly, outpacing periodic assessments. Access often drifts silently as roles change or new features are enabled. A scope that seemed safe last week could quietly expand (e.g. an AI plugin getting new permissions after an update) without anyone noticing.
All of these factors mean that static SaaS security and governance tools are lagging behind. If you only look at static application configurations, predefined roles, and logs after the fact, you can’t reliably know what an AI agent actually did, what data it accessed, what records it modified, or whether its permissions exceeded policy in the meantime.
A checklist for securing co-pilots and AI agents
Before introducing new tools or frameworks, security teams should test their current posture.
If many of these questions are difficult for you to answer, it’s a sign that static SaaS security models are no longer enough for AI tools.
Dynamic AI-SaaS Security – Guardrails for AI Applications
To address these gaps, security teams are beginning to adopt what can be described as dynamic AI-SaaS security.
Unlike static security (which treats applications as siled and immutable), IA-SaaS dynamic security is an adaptive, policy-driven layer of protection that operates in real-time on top of your SaaS integrations and OAuth grants. Think of it as a living security layer that understands what your co-pilots and agents are doing at any moment, and adjusts or intervenes based on policy.
Dynamic AI-SaaS security monitors AI agent activity across all your SaaS applications, watching for policy violations, anomalous behavior, or signs of trouble. Rather than relying on yesterday’s permissions checklist, it learns and adapts to how an agent is actually used.
A dynamic security platform will track the actual access of an AI agent. If the agent suddenly touches a system or data set outside its usual scope, it can report or block it in real time. It can also instantly detect configuration or privilege drift and alert teams before an incident occurs.
Another feature of AI-SaaS dynamic security is visibility and auditability. Since the security layer arbitrates the actions of the AI, it keeps a detailed record of what the AI does across systems.
Every prompt, every file viewed, and every update the AI makes can be saved in structured form. This means that if something goes wrong, for example if an AI makes an unintentional change or accesses a prohibited file, the security team can trace exactly what happened and why.
Dynamic IA-SaaS security platforms themselves leverage automation and AI to track the torrent of events. They learn normal agent behaviors and can prioritize real anomalies or risks so security teams aren’t swamped with alerts.
They can correlate an AI’s actions across multiple applications to understand context and report only real threats. This proactive stance helps detect issues that traditional tools might overlook, whether it’s a subtle data leak via AI or a malicious prompt injection causing an agent to misbehave.
Conclusion – Adopt adaptive guardrails
As AI co-pilots play a larger role in our SaaS workflows, security teams should think about evolving their strategy in parallel. The old, forgotten, customizable SaaS security model, with static roles and infrequent audits, simply cannot keep up with the speed and complexity of AI activity.
The case for dynamic IA-SaaS security is ultimately about maintaining control without stifling innovation. With the right dynamic security platform in place, organizations can confidently adopt co-pilots and AI integrations, knowing they have real-time guardrails to prevent misuse, detect anomalies, and enforce policies.
Dynamic AI-SaaS security platforms (like Reco) are emerging to deliver these out-of-the-box capabilities, from AI privilege monitoring to automated incident response. They act as the missing layer on top of OAuth and application integrations, adapting on the fly to what agents are doing and ensuring nothing slips through the cracks.
| Figure 1: Reco Generative AI Application Discovery |
For security leaders watching the rise of AI co-pilots, SaaS security can no longer be static. By adopting a dynamic model, you equip your organization with living guardrails that allow you to ride the AI wave safely. This is an investment in resilience that will pay off as AI continues to transform the SaaS ecosystem.
Want to know how dynamic AI-SaaS security could work for your organization? Consider exploring platforms like Reco, designed to provide this adaptive guardrail layer.
Request a demo: get started with Reco.
