The days of treating AI bots and copilots as handy tools are over. We’re officially in the age of the machine colleague, where intelligent tools aren’t just completing tasks, they’re delegating work, organizing teams, and even making judgment calls.
That’s why identity assurance for AI has become so essential. When an AI writes the summary, assigns the work, and frames what “happened,” it becomes part of the decision chain. That’s not a productivity layer. That’s authority by proxy, and authority needs governance.
The trouble is that UC and collaboration platforms were built to answer a simpler question. Who logged in? They were never designed to prove who actually decided, approved, or acted when humans and systems are working side by side.
Attackers have noticed. Microsoft has publicly warned about financially motivated groups using Teams phishing to establish footholds. Verizon’s DBIR still shows social engineering as one of the most reliable ways in. When trust is assumed, it’s easy to borrow.
Further reading:
Identity Assurance for AI: The Identity Crisis in UC
Unified communications runs on borrowed trust.
If someone’s in the meeting, camera on, using the right name, we assume legitimacy. If a message shows up in Teams or Slack, we treat it as internal by default. That mental shortcut made sense when collaboration tools were mostly human, mostly synchronous, and mostly disposable.
AI shattered the old trust signals first. Voice and video feel convincing until they aren’t. Writing style looks authentic until a model learns it better than the person it’s copying. The Arup deepfake meeting fraud didn’t work because people were careless. It worked because meetings still feel final. Authority plus urgency shuts down doubt fast. Roughly $25 million moved because everyone in the room believed presence equaled proof.
At the same time, UC identity assurance still treats authentication like a box you tick once. Log in. Pass MFA. From that moment on, the system mostly stops asking questions. But collaboration doesn’t stay in one lane. A status call turns into a budget decision. A “quick sync” turns into approval to change vendor payment details.
Now layer in AI agent identity. Copilots summarizing conversations. Bots assigning tasks. Agents kicking off workflows. Actions still look human. Outcomes still land in human spaces. But responsibility starts to smear. Was that decision made by a person, shaped by an AI, or executed automatically because no one slowed it down?
This is where non-human identity turns into a governance problem. If you can’t clearly prove who initiated, who approved, and who actually acted, investigations become archaeology.
How to Structure Identity Assurance for AI and Humans in UC
Once you look for it, the pattern is hard to ignore. Human identity failures and machine identity failures don’t cause different problems. They cause the same problems, just at different speeds.
Most AI agents aren’t named in identity systems. They inherit permissions, act through APIs, and don’t have an obvious owner. When something goes wrong, there’s no clean answer to a simple question: who was responsible for that action?
That’s the real break. When identity falls apart, access control isn’t the first thing you lose. You lose the story. You lose the ability to explain how a decision actually happened. And once that’s gone, everything that follows gets heavier than it should be. Reviews drag. Investigations stall. Risk creeps in where it never needed to exist.
Defining Identity Types in Modern UC
This is where things usually start going wrong, long before anyone talks about AI risk or attackers. People don’t agree on who or what is actually allowed to act inside collaboration. So everything gets lumped together, and nobody notices until something breaks. You’ve got:
- People with accounts: Employees, contractors, execs. The obvious ones. The problem isn’t that they exist; it’s that authority slides around inside meetings without anyone naming it.
- Guests who quietly become insiders: Vendors, partners, advisors. Someone invites them to a channel or a recurring call because it’s faster than forwarding notes. Weeks turn into months.
- Bots and integrations that never go away: These are the ones everyone forgets about. A workflow gets added to keep tickets moving. A bot posts summaries. An integration syncs data between systems. Nobody removes access when the project ends because nothing breaks obviously.
- AI agents acting for people: Agents write summaries, create tasks, and update records without waiting for someone to double-check them. Gartner says this kind of agentic behavior will be built into a huge share of enterprise software within a year or two. Yet most teams still rely on vibes instead of explicit delegation.
That’s how non-human identity turns into a problem without anyone meaning it to. Not through some dramatic failure, but through a hundred small decisions nobody thought needed rules.
Attribution that Separates Action from Authority
Attribution forces you to slow down and be precise, and collaboration culture hates that. Everyone just wants to keep things moving. But when UC identity assurance fails, it’s usually because attribution was unclear long before anything went wrong.
In modern UC, especially once AI agent identity enters the picture, there are at least three roles tangled together unless you deliberately pull them apart:
- The actor of record: The thing that executed the action. Sometimes that’s a person. Increasingly, it’s an agent or workflow. Meeting summaries posted automatically. Tickets created without anyone touching them.
- The initiator: The human who set things in motion. The person who asked for the summary. The manager who said, “Can you follow up on this?” That intent often lives in conversation, not in logs.
- The approver: The one who had the authority to say yes. This is where things get risky. In meetings, approval is often implied. A nod. Silence. A rushed “sounds good.” Collaboration tools were never built to capture these moments as formal approvals, even though the business treats them that way later.
When those roles blur, accountability evaporates. It gets worse with non-human identity, because agents don’t hesitate. They act cleanly, quickly, and without context. The output looks legitimate. The artifact travels. By the time someone questions it, the decision has already hardened.
Good attribution doesn’t mean slowing work to a crawl. It means being honest about who initiated, who approved, and what actually carried out the action.
Session-Level Identity Confidence
This is where a lot of identity thinking is stuck, and it shows. Log in. Pass MFA. Box checked. From there, every moment gets treated like it carries the same level of risk.
Anyone who’s survived a long meeting knows that’s a fantasy. Ten minutes of routine updates. Someone drops in late. A casual question about timing turns into a real call about money or priority. Then the meeting ends, the transcript gets saved, and suddenly it all looks tidy. Like the decision was obvious. Like it was planned that way.
Now add AI agent identity. Agents don’t feel hesitation. They don’t sense discomfort. If they’re allowed to act, they act. Summaries get posted. Tasks get created. Follow-ups go out while people are still packing up their thoughts.
Session-level identity confidence is about admitting that trust should rise and fall with risk. A brainstorming discussion doesn’t need friction. A decision that moves money, data, or authority absolutely does.
The mistake teams make is adding controls everywhere. That just drives people toward workarounds. The smarter move is narrower. Decide which moments matter. Treat those moments differently. Ask for stronger proof when the stakes jump.
Identity Logging That Produces Defensible Evidence
Most logging strategies look fine right up until someone actually needs them.
On paper, everything is there. Timestamps. User IDs. Event trails. In practice, when an incident hits or an audit lands, the same problem shows up. You can see what happened, but not why, and not with enough confidence to stop the follow-up questions.
Identity assurance for AI and humans working together needs more depth.
Collaboration tools are great at capturing outcomes. Messages sent. Meetings recorded. Files shared. What they’re bad at is preserving intent. Who asked for the summary? Who approved the action? Whether an AI agent identity was acting independently or carrying out someone else’s instructions. That context is usually missing by the time logs are reviewed.
It gets worse once artifacts start moving. A transcript becomes a summary. A summary becomes an action item. An action item turns into a ticket. Each step looks legitimate on its own. The identity trail doesn’t always survive the journey.
Good logging doesn’t mean capturing more data. It means capturing the right relationships.
- Which identity initiated the action
- Which identity executed it
- What authority existed at that moment
- Whether delegation was explicit or implied
That applies just as much to non-human identity as it does to people. Especially to non-human identity, honestly, because agents don’t leave breadcrumbs unless you design the system to do it for them.
Discover:
Designing Identity Assurance for AI and Humans: Ensuring Accountability
This doesn’t get fixed with bigger tools or louder policy. It gets fixed by designing collaboration the way people actually use it.
What consistently works looks like this:
- Make every actor visible: Every human, bot, and agent needs a clear identity and a named owner. If an AI agent identity can read conversations, post summaries, or trigger workflows, someone has to be accountable for its behavior. “The system did it” isn’t an answer that survives incidents.
- Treat delegation as a real control: Delegation can’t live in the gray area anymore. If an agent is acting for a person, that authority has to be spelled out. What it can do. What it can’t. Who owns the outcome?
- Match identity confidence to decision risk: Most collaboration doesn’t need friction. Some moments absolutely do. Payments, vendor changes, external sharing, and executive direction. These should never rely on silence or assumption as approval.
- Design for investigation, not optimism: Assume summaries will be questioned. Assume decisions will be challenged. Logging should answer who initiated, who approved, and what was acted upon without interpretation or guesswork.
- Make the safe path the easy path: When approved workflows are awkward, people route around them. That’s how shadow AI and copy-paste habits quietly destroy Identity assurance in UC without triggering alarms.
None of this slows collaboration down. It removes ambiguity, which is what turns ordinary work into risk later, when someone finally has to explain what happened and why.
Where is Identity Assurance for AI in UC Headed Next?
Some things are already settled, whether teams like it or not.
AI agents are going to spread fast. Gartner expects 40% of enterprise applications to include task-specific AI agents by 2026. In collaboration, that means meetings won’t just produce notes. They’ll kick off workflows, approvals, updates, and follow-ups automatically. AI agent identity becomes structural, not optional.
Regulation is tightening in a different way. The European Union AI Act and similar frameworks aren’t focused on whether AI exists. They focus on whether outcomes are auditable. Who oversaw the system? Who approved the decision? Whether the trail holds up later.
Meanwhile, non-human identity is already outnumbering people. Bots, agents, integrations, service accounts. The teams that move fastest won’t be the ones with the most AI features. They’ll be the ones who can explain, quickly and calmly, who did what when humans and machines worked together.
If you want a broader view of how identity, compliance, and collaboration risks are converging across UC, this is a good place to go next: The Ultimate Guide to UC Security, Compliance, and Risk.
