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18/08/2026 2:29 pm
TL;DR: Communicative technology is shifting from human-only interaction to systems where people, AI, and agents exchange decisions and content directly, according to ActiveFence, which increases exposure to deepfakes, data leakage, bias, and trust erosion. The governance challenge is no longer just moderation or user safety, but building security, compliance, and identity-aware controls into AI-mediated communication from the start.
NHIMG editorial — based on content published by ActiveFence: Into the Looking Glass and the Definition of Communicative Technology
Questions worth separating out
Q: What breaks when AI agents are allowed to contain incidents without governance?
A: The response chain becomes difficult to audit and reverse.
Q: Why do local AI agents complicate identity and access management?
A: They can retain legitimate permissions while changing timing, prioritisation, and action sequence outside human presence.
Q: How do security teams know whether an AI agent is operating safely?
A: Security teams know an AI agent is operating safely when its permissions, invoked tools, and accessed data remain consistent with the approved use case over time.
Practitioner guidance
- Map AI communication pathways and delegated authority Inventory where humans, models, and agents exchange content that can trigger decisions, routing, or publication.
- Apply identity governance to agent participation Treat AI agents that read, summarize, draft, or act on messages as governed machine identities.
- Enforce provenance controls on generated content Require traceability for where model output came from, which inputs shaped it, and what downstream system consumed it.
What's in the full article
ActiveFence's full article covers the conceptual and safety framing this post intentionally leaves at a higher level:
- How the vendor defines communicative technology across human-human, human-AI, and AI-AI interactions
- The article's discussion of deepfakes, bias, and data leak scenarios in AI-mediated communication
- The vendor's broader trust and compliance framing for teams building GenAI and agentic interfaces
- Additional context on the company's Alice positioning and related content on AI safety
👉 Read ActiveFence's analysis of communicative technology and AI trust risk →
AI-to-AI communication is becoming a governance problem, not just a UX one?
Explore further
18/08/2026 3:17 pm
Trust is becoming an identity control surface, not just a product feature. As AI systems begin participating in conversations, the question is no longer whether content is safe to read but whether the system producing or transforming it is authorised to do so. That pushes communicative technology into the same governance conversation as IAM and NHI, because attribution, delegation, and accountability now sit inside the interaction model. Practitioners should treat provenance as a security control, not a UX enhancement.
A question worth separating out:
Q: Who is accountable when an AI-assisted workflow leaks sensitive data?
A: Accountability sits with the organisation that allowed the workflow to operate outside governed controls. Security, IAM, and business owners all share responsibility for ensuring approval, logging, and lifecycle management exist before data moves through the path. If no one can block or revoke it, no one is governing it.
👉 Read our full editorial: Communicative AI expands trust risk across human, AI, and agent interactions
