Latest Case For Dynamic Ai-saas Security As Copilots Scale 2025

Within the past year, artificial intelligence copilots and agents have quietly permeated the SaaS applications 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 every major SaaS vendor has rushed to embed AI into their offerings.

The result is an explosion of AI capabilities across the SaaS stack, a phenomenon of AI sprawl where AI tools proliferate without centralized oversight. For security teams, this represents a shift. As these AI copilots scale up in use, they are changing how data moves through SaaS. An AI agent can connect multiple apps and automate tasks across them, effectively creating new integration pathways on the fly.

An AI meeting assistant might automatically pull in documents from SharePoint to summarize in an email, or a sales AI might cross-reference CRM data with financial records in real time. These AI data connections form complex, dynamic pathways that traditional static app models never had.

This shift has exposed a fundamental weakness in legacy SaaS security and governance. Traditional controls assumed stable user roles, fixed app interfaces, and human-paced changes. However, AI agents break those assumptions. They operate at machine speed, traverse multiple systems, and often wield higher-than-usual privileges to perform their job. Their activity tends to blend into normal user logs and generic API traffic, making it hard to distinguish an AI's actions from a person's.

Consider Microsoft 365 Copilot: when this AI fetches documents that a given user wouldn't normally see, it leaves little to no trace in standard audit logs. A security admin might see an approved service account accessing files, and not realize it was Copilot pulling confidential data on someone's behalf. Similarly, if an attacker hijacks an AI agent's token or account, they can quietly misuse it.

Moreover, AI identities don't behave like human users at all. They don't fit neatly into existing IAM roles, and they often require very broad data access to function (far more than a single user would need). Traditional data loss prevention tools struggle because once an AI has wide read access, it can potentially aggregate and expose data in ways no simple rule would catch.

Permission drift is another challenge. In a static world, you might review integration access once a quarter. But AI integrations can change capabilities or accumulate access quickl

Source: The Hacker News