By: Muhammad El-Bassiony, Presales Manager, Mindware
Artificial intelligence is changing how organisations operate, with AI agents increasingly capable of executing tasks, interacting with applications and making decisions with limited human intervention. Yet as enterprises embrace this new wave of automation, an important security question is emerging: how do organisations ensure that autonomous systems have the right access, and nothing more?
Traditional identity and access management (IAM) was designed around relatively predictable behaviour. Employees log in to access applications, service accounts perform predefined tasks, and machine identities communicate with known systems. AI agents challenge these assumptions. They can initiate multiple actions across different platforms, generate sub-tasks and adapt their behaviour in ways that may not have been explicitly anticipated when access was granted.
This changes the nature of identity security. The challenge is no longer simply verifying who or what has access, but governing what an autonomous entity does with that access, in real time. Identity and security operations must therefore become more closely connected, with behavioural monitoring playing a central role alongside authentication and authorisation.
Establishing a distinct identity for every agent
One of the most important principles of agentic AI security is that every AI agent must have its own identity and credentials. Allowing agents to inherit an employee’s credentials or operate through a shared service account creates significant accountability gaps. If an incident occurs, security teams may struggle to establish which agent performed an action, who authorised it and what purpose it served.
Each agent should instead have a distinct, discoverable non-human identity, supported by an auditable delegation chain linking its activities to the human or process that initiated it. This establishes accountability from the outset and gives security teams the information they need to investigate incidents and enforce policy.
At Mindware for example, we work with channel partners to help organisations develop appropriate agent identity models using the relevant technologies and expertise across our portfolio. Building these controls into the initial design is considerably more effective than attempting to reconstruct accountability after autonomous systems have already become embedded in business operations.
Replacing standing privileges with task-based access
Traditional access models often rely on permissions that remain in place long after they are needed. For autonomous agents, this approach introduces unnecessary risk. An agent with excessive standing privileges can potentially access sensitive systems or perform actions well beyond its intended purpose.
A more effective model combines least-privilege access with just-in-time authorisation. An agent should receive only the permissions required for a specific task, for a limited period, with credentials that expire when the work is completed. Access rights should also be linked to the task’s context and reviewed continuously.
This is particularly important as agents interact with multiple systems and potentially accumulate permissions over time. Without regular oversight, the resulting access profile can become a route to privilege escalation.
As a value-added distributor, Mindware helps partners map these requirements to appropriate identity governance and privileged access management technologies. Our presales and services capabilities also support proof-of-concept initiatives and deployments, helping organisations translate security principles into practical controls.
Making monitoring and revocation part of everyday operations
AI agents can operate at machine speed, meaning periodic access reviews alone are insufficient. Organisations need continuous monitoring that establishes a baseline for each agent’s normal behaviour and identifies deviations that may indicate misuse, compromise or unexpected activity.
Equally important is the ability to intervene quickly. Security teams must be able to suspend an agent or revoke its access without necessarily disabling the wider application or business service it supports. Short-lived credentials strengthen this capability by reducing reliance on long-standing secrets that must be tracked down and invalidated individually.
Looking beyond authentication to agent integrity
A valid identity does not guarantee that an AI agent is behaving safely. Agents can be manipulated through prompt injection, exposed to poisoned data or compromised through the tools and services they access. Their credentials may remain legitimate even when their actions have been influenced by malicious instructions.
Enterprises therefore need a layered approach that combines identity controls with behavioural analytics, input validation, tool security and enforceable guardrails. These safeguards should restrict what an agent can do, regardless of the instructions it receives or the content it processes.
Mindware brings together technologies across threat detection, network security and data protection to help partners develop layered defences suited to specific customer requirements. The objective is not simply to introduce more security products, but to ensure that the controls work together to address the risks presented by autonomous systems.
Defining ownership before accountability becomes a problem
Governance is another area where organisations need to act early. AI agent identities cannot be left between IT, security and business teams without clear responsibility. Security teams should establish the policy framework, IAM and platform teams should operate the relevant controls, and the business units deploying agents should remain accountable for the associated operational risks.
Every agent should have a named human or business owner responsible for its purpose, permissions and behaviour. This cross-functional model helps ensure that accountability remains clear as agent deployments expand.
Preparing for a future dominated by non-human identities
The most pressing risks are already clear: shadow agents deployed without security oversight, excessive privileges that persist beyond operational requirements, and agents without identifiable owners or reliable audit trails. These challenges will become more difficult as organisations manage growing numbers of non-human identities, potentially exceeding their human identities.
Enterprises should begin by discovering and inventorying the agents operating across their environments. They should extend identity governance to non-human identities, replace long-lived secrets with ephemeral credentials where appropriate, establish clear ownership and invest in continuous behavioural monitoring.
For CISOs, the strategic consideration is straightforward: agentic AI is not merely another technology to secure. It introduces a new class of actor into the enterprise, one capable of taking actions across systems at unprecedented speed and scale.
Organisations that treat AI agent identity as a core governance priority today will be better positioned to scale innovation securely, maintain accountability and avoid the costly consequences of trying to regain control after autonomous systems have spread throughout the enterprise.
