Topic starter
14/07/2026 4:22 pm
TL;DR: The rise of AI assistants and agents creates a new agentic workspace where human and machine risks overlap across email, collaboration, data, and SaaS applications, according to Proofpoint. The security model now has to govern both people and AI agents, because the attack surface expands faster than legacy workspace controls can absorb, while Q3 2025 results showed double-digit ARR growth and broad enterprise adoption.
Editorial analysis by NHI Mgmt Group, based on content published by Proofpoint: “Evolving, growing, and innovating: Proofpoint’s strong Q3 2025 performance”.
Key questions
Q: How should security teams govern AI agents in shared workspaces?
A: Security teams should treat the workspace audience as part of the authorization decision.
Q: Why do AI agents increase workspace security risk for data teams?
A: AI agents can retrieve, transform and redistribute sensitive content at machine speed, which increases the chance that a single policy failure becomes many exposures.
Q: What are the warning signs that agentic workspace controls are failing?
A: The strongest warning signs are unmanaged assistants, unclear data boundaries, excessive agent permissions and weak logging for agent-driven actions.
Practitioner guidance
- Map AI assistants and agents to workspace permissions Identify every assistant, copilot and autonomous agent that can read, summarise or act inside email, collaboration and SaaS systems.
- Extend DLP and DSPM to non-human workflows Review whether current content inspection and sensitive-data controls can distinguish between human sharing and agent-driven transformation, forwarding or retrieval.
- Harden prompt and content handling policies Treat prompts, replies, attachments and summaries as governed inputs and outputs.
Bottom line: The article frames AI assistants and agents as part of the workspace threat model, not as peripheral automation.
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14/07/2026 4:30 pm
The agentic workspace creates a governance overlap that legacy IAM never had to solve. Traditional identity programmes were built around humans requesting access and systems enforcing it at sign-in or session creation. Once AI agents operate inside the same workspace, identity decisions move into runtime behaviour, delegation and content handling. That makes the issue more than access management. It becomes a control problem across human identity, non-human identity and data movement, with direct implications for IAM, PAM, DLP and DSPM teams. Practitioners should treat this as a governance redesign, not a point-product adjustment.
A question worth separating out:
Q: What should organisations do when human and non-human workflows share the same workspace?
A: Organisations should create a joint governance model for identity, collaboration and data security. That model should define approval, review, monitoring and offboarding for both people and agents, because shared workspaces fail when controls assume only one type of actor can move information or make decisions.
👉 Read our full editorial: Proofpoint’s agentic workspace pivot puts AI agent security in scope