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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.
NHIMG editorial — based on content published by Proofpoint: the company’s Q3 2025 update and agentic workspace commentary
By the numbers:
- Proofpoint said its Q3 2025 results included double-digit ARR growth year over year, driven by strong adoption of its Data Security portfolio and Proofpoint Prime Threat Protection.
- Gartner named Proofpoint a Leader in its 2025 Magic Quadrant for Digital Communications Governance and Archiving Solutions for the second consecutive year.
Questions worth separating out
Q: How should security teams govern AI assistants that can access audit data?
A: Treat them as privileged non-human identities with defined scope, logging, and approval boundaries.
Q: Why do conversational AI systems create new identity and access risks?
A: Because they can combine data retrieval, decision-making, and execution in a single interaction.
Q: What breaks when prompt injection is not governed like an access problem?
A: The organisation may treat malicious text as a harmless message, even though it can steer an agent into exposing data or taking privileged actions.
Practitioner guidance
- Map agent permissions to specific tasks Inventory every assistant, copilot, and workflow agent that can access mail, files, chat, or SaaS applications, then limit each one to a named purpose and minimal dataset.
- Extend identity governance to non-human actors Create an approval and review process for AI agents that mirrors service account governance, including ownership, scope, exception handling, and offboarding.
- Join content security to authorisation policy Make prompt injection and malicious content part of the same control discussion as access decisions, so untrusted inputs cannot trigger privileged downstream actions.
What's in the full article
Proofpoint's full post covers the operational detail this post intentionally leaves for the source:
- The specific product and platform changes behind the agentic workspace strategy, including how the controls are positioned across email, collaboration, and data security.
- Q3 performance detail on customer adoption, retention, and partner motion that supports the business case for the direction of the platform.
- Named examples of the first agentic AI capabilities and how Proofpoint describes their intended operational use.
- Gartner citations and market-positioning context that are useful if you are comparing categories or tracking vendor messaging.
👉 Read Proofpoint’s analysis of the agentic workspace and AI agent security →
Agentic workspace security: what changes for IAM and data teams?
Explore further
14/07/2026 4:30 pm
The agentic workspace is becoming an identity governance problem, not just a productivity story. Once AI assistants can read, summarise, and act on enterprise data, they behave like governed non-human identities whether the organisation labels them that way or not. That shifts the control burden onto IAM, PAM, and data security teams, because access scope and auditability now need to extend to software entities that operate inside human workflows. The practitioner conclusion is simple: if the agent can act, it must be governed like an identity.
A few things that frame the scale:
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate, according to AI Agents: The New Attack Surface report.
- 92% agree governing AI agents is critical to enterprise security, yet only 44% have implemented any policies to do so.
A question worth separating out:
Q: Who is accountable when an AI agent accesses sensitive data it was not meant to use?
A: Accountability sits with the team that approved the agent, its connectors, and its policy boundaries, not with the runtime behaviour alone. Organisations need ownership for intent, permissions, monitoring, and validation so they can prove whether the agent stayed inside its approved purpose. Without that, audit and regulatory response become retrospective guesswork.
👉 Read our full editorial: Proofpoint’s agentic workspace pivot puts AI agent security in scope
