Agentic AI
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Recruitment & Reskilling Strategy
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Training & Security Leadership
Agentic AI Is Creating New Risks, New Career Opportunities for Cyber Professionals
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June 11, 2025
Artificial intelligence is no longer confined to sandboxes. It’s writing code, triaging support tickets, filtering threats, and, in some organizations, autonomously managing parts of the network. As AI shifts from passive tool to active participant, cybersecurity professionals must confront a critical identity management question: Does AI need to be treated like any other user in your ecosystem – with secure access?
See Also: OnDemand | Navigate the threat of AI-powered cyberattacks
The Rise of Agentic AI
The shift from AI as a statistical model to AI as an agent marks a turning point in cybersecurity strategy. Agentic AI refers to systems capable of autonomous goal pursuit, meaning they can perceive, decide and act without step-by-step human instruction. These agents don’t just analyze data. They initiate actions that affect operational environments.
From large language models embedded in customer service bots to AI agents adjusting real-time infrastructure configurations, agentic AI is already functioning with levels of autonomy that demand identity, authentication and oversight.
The problem? Most security teams haven’t updated their identity and access governance models to account for non-human identities with this level of autonomy.
When AI Acts Like a User
The real-world implications of agentic AI are no longer theoretical. Across industries, AI systems are beginning to operate with increasing levels of autonomy – making decisions, executing tasks and interacting with other systems without human oversight. These capabilities introduce significant challenges for identity and access management, particularly when organizations fail to distinguish between tool-based AI and agent-based AI.
Consider three possible scenarios:
- An agentic AI in a SOAR platform autonomously closes security tickets and adjusts firewall rules. If this AI is treated as a passive tool, it may bypass access controls or audit trails designed for human users, leaving the organization blind to unintended or harmful actions.
- A machine learning agent dynamically modifies pricing or resource allocation policies without human review. Without a unique identity and access boundary, the agent could exceed its mandate, leading to compliance violations or cascading operational errors.
- A generative agent composes, formats and distributes internal memos that include sensitive supplier data or contract terms. If it’s not governed like a user, the AI could inadvertently share proprietary or third-party information with unauthorized recipients, exposing the organization to supplier trust breaches, contractual violations or downstream regulatory scrutiny.
These AI agents aren’t tools executing user commands. These are entities initiating actions on their own, and that means they must be governed like users, with identities, policies and privileges that reflect their autonomy.
IAM Strategies for Agentic AI
If AI is your new coworker, it needs an identity and clear boundaries. Here are key takeaways for security professionals:
- Assign a unique identity to each AI agent or service. Treat it like a user or system account with a defined lifecycle.
- Apply least privilege principles rigorously. Just because the AI can do something does not mean it should. Limit access to only what it needs to achieve defined objectives.
- Use RBAC or ABAC to enforce contextual access. This adds a layer of policy control that evolves with the agent’s use cases.
- Log and monitor AI activity independently. You need full traceability of actions taken by agentic systems, especially when they operate asynchronously.
- Include AI in incident response and privilege escalation planning. If an agent goes rogue or gets compromised, you must be able to isolate and revoke access immediately.
What This Means for Your Career
If you’re in cybersecurity today, particularly in identity, DevSecOps or governance, the rise of agentic AI marks a shift in scope and responsibility. You’re no longer just managing access for people and applications. You’re managing digital actors with decision-making capabilities. That shift creates both a risk and an opportunity.
The risk is clear. If your IAM model treats AI as infrastructure instead of a user, you could be held accountable when it makes an unsanctioned move such as modifying settings, exposing sensitive data or making high-impact decisions outside its scope. As regulators catch up to AI’s operational role, the margin for error will shrink.
But there is also an emerging career advantage in AI agents. Professionals who understand how to govern AI identity – who can design access boundaries, log accountability and implement containment strategies – will be in high demand. These roles bridge the gap between technical implementation and policy enforcement. They require fluency in both AI system behavior and access governance frameworks.
This is your opportunity to lead the development of machine identity governance in your organization. Think of it as the next frontier in zero trust, extending identity-aware security to agents, not just humans. The people who can operationalize that model will shape the future of digital risk management.
If your AI agent decided to act on its own tomorrow, would your systems know who it was, what it did, and whether it had the right to do it? If the answer is no, it’s time to give your new coworker a badge and a policy framework that matches its autonomy.
Ready to upskill?
Explore CyberEd.io’s training on AI implementation, identity governance and securing machine-driven systems. The future of cybersecurity includes AI so make sure your career does too.
