Agentic AI is moving from experimentation into enterprise operations as organisations look to improve efficiency, increase output, accelerate decision-making, and scale automation. But as agents are given access to systems and data, they create a new identity security challenge.
Unlike traditional software, AI agents can reason, plan, initiate actions, and adapt their behaviour based on changing inputs. Recent IBM research found that only 11% of technology leaders feel completely prepared for AI agent deployment at scale, while surveyed organisations experienced an average of 54 AI agent incidents last year.
From an identity perspective, the issue’s straightforward: if an agent can access data or act across connected systems, it needs to be treated as an identity. That means knowing who owns it, what it’s allowed to do, and how its behaviour will be controlled over time. In practice, this gets difficult because AI adoption doesn’t always follow a centralised path. Agents can enter the environment through everyday tools and workflows before security teams have full visibility of where they’re operating.
The result is a growing layer of unmanaged AI activity. This is where Shadow AI starts to emerge, as agents and AI-enabled tools are adopted without proper oversight. Organisations can’t secure what they can’t see, and they can’t scale what they can’t control — which is why identity security has to become the foundation for Agentic AI readiness.
At their core, AI agents are non-human identities. They’re not human users, but they still need to be governed as identities because they can access systems, retrieve data, trigger workflows, and take action.
They sit closer to machine identities than human identities, but autonomy changes the risk. AI agents can act at speed, adapt to changing context, and make decisions across connected systems.
That’s why organisations need to slow down enough to speed up safely. Before AI agents scale across workflows, applications, and data sources, they need identity foundations that protect visibility, control, and accountability.
At ProofID, we organise that approach into five foundational pillars:
By combining these pillars, we establish the identity security foundation needed to operationalise Agentic AI safely and at scale.
The five pillars of Agentic AI readiness provide a framework for securing and scaling AI agents as non-human identities:
| Pillar | Purpose |
| Discover | Identify all AI agents, understand where they operate, and map their interactions across the environment. |
| Govern | Establish ownership, lifecycle management, policies, and accountability for every AI agent. |
| Connect | Build consistent, secure connections between AI agents and the systems, data, and tools they need to reach. |
| Trust | Make real-time authorisation decisions based on context, risk, and least-privilege principles. |
| Observe | Continuously monitor agent behaviour, detect anomalies, and intervene when necessary. |
Together, these pillars create the foundation necessary to implement Agentic AI safely and confidently.
Every security programme begins with visibility. Before organisations can secure AI agents, they need to understand where they exist, what they’re connected to, and how they interact with enterprise resources.
This sounds straightforward, but AI visibility gaps are already emerging. Business teams are adopting AI tools independently, developers are creating autonomous workflows, third-party applications are introducing embedded agents, and experimental projects can move into production without formal oversight.
The result is a growing population of AI identities operating outside established governance processes. This is where Shadow AI starts to emerge, as AI-enabled activity appears without formal visibility or control. To understand how well an organisation can discover and manage this activity, security leaders should ask:
Without comprehensive discovery, every subsequent security control becomes more difficult to enforce. Visibility is the starting point for every other pillar.
Discovery identifies which agents exist. Governance determines who’s responsible for them. Every AI agent in an organisation should have a clearly defined owner, a documented purpose, and a managed lifecycle. Without a named owner, an AI agent can become an unmanaged identity with persistent access and unclear accountability.
Best practice says employees shouldn't retain access indefinitely after changing roles or leaving the organisation, which is why most organisations run periodic certification campaigns to confirm access is still needed and still correct. AI agents need the same discipline. On a regular cycle, someone should be certifying that each agent is still doing the job it was created for, that its access still matches that job, and that it hasn't quietly picked up permissions it no longer needs. If an agent can't be recertified, that's the signal to retire it.
Governance should address:
Many organisations are discovering that AI agents often outlive the projects they were created for. Without lifecycle controls, dormant agents can accumulate permissions, maintain unnecessary integrations, and create long-term security exposure.
Questions security leaders should ask include:
Governance establishes accountability and creates the operational structure required to manage AI at scale.
As organisations deploy more agents, complexity increases. New integrations form between applications, cloud services, data repositories, automation platforms, and emerging agent infrastructure such as MCP servers. Without the right architecture, these connections can quietly multiply into a tangle of one-off integrations, each built a little differently, each with its own gaps.
This is where thoughtful connection design matters. Organisations need shared, well-built pathways, such as secure gateways and common integration points, so that every agent reaches the systems it needs in the same trusted way, wherever that connection happens.
Connection capabilities should include:
The aim is to make sure no agent's access depends on how one team happened to wire it up. Every agent should connect the same safe way, everywhere. Key questions to consider include:
Without well-designed connections, governance is a good intention that never quite reaches the agents doing the work.
Traditional access models often rely on standing permissions: access that remains in place until someone manually changes or removes it. That approach becomes increasingly problematic as AI agents gain autonomy. For autonomous agents, trust should be continuously assessed rather than permanently granted.
Agents operate continuously. They interact with multiple systems and their activities can vary significantly depending on context, data inputs, and business requirements. As a result, this demands a more dynamic trust model.
Organisations need real-time authorisation decisions based on current conditions rather than static assumptions. This includes:
Instead of simply asking whether an agent has access, organisations should evaluate whether the requested action is appropriate at that specific moment.
Important questions to ask include:
Trust provides the decision-making layer that allows AI agents to operate safely while maintaining business agility.
Security doesn’t end when an AI agent goes live. In production, organisations need to understand not just whether an agent is active, but whether it’s behaving as expected. That means monitoring patterns of activity, detecting anomalies, identifying policy violations, and intervening quickly when behaviour moves outside agreed boundaries.
The speed of detection matters. If an AI agent behaves unexpectedly, the difference between a contained event and a serious incident may come down to how quickly the organisation can spot the change, understand what happened, and intervene.
As AI systems become more sophisticated, continuous oversight becomes increasingly important.
Observation capabilities include:
To assess the ability to observe Agentic AI activity within an organisation, some critical questions include:
Having the capability to observe and respond transforms Agentic AI security, allowing organisations to be proactive.
Strong identity foundations help organisations move faster with Agentic AI because they create the confidence to scale safely. When security teams can evidence how agents are governed, AI adoption becomes easier to support and safer to expand.
They can confidently introduce new use cases faster, onboard additional agents more efficiently, and demonstrate accountability to regulators, auditors, customers, and executive stakeholders.
The key difference is the maturity of the identity foundation supporting Agentic AI. This creates the conditions for AI agents to be integrated into core business processes with predictable control and oversight.
The shift from AI experimentation to AI operationalisation is already underway. AI agents are becoming active participants in business processes, application ecosystems, and enterprise decision-making. Their role will only expand as organisations pursue greater automation and efficiency.
Agentic AI is moving beyond experimentation and into the systems, workflows, and decisions that keep the business running. The question for technology and security leaders isn’t simply whether AI agents will become part of the enterprise environment. It’s whether the organisation can see them, govern them, control what they can do, verify when they should be trusted, and respond when behaviour changes.
Those identity foundations are what turn Agentic AI from an unmanaged risk into something the business can scale with confidence.
How prepared is your organisation to scale Agentic AI securely? Assess your maturity across the five pillars of Agentic AI readiness — Discover, Govern, Connect, Trust, and Observe — and identify the gaps that could limit your ability to expand AI initiatives with confidence.
Take the Agentic AI Readiness Assessment to benchmark your maturity across the five pillars and identify the gaps that could limit secure AI adoption.