What Is Agentic AI?
Most people associate artificial intelligence with tools like ChatGPT, generative AI that responds to prompts.
Agentic AI operates differently. An AI agent is given a goal and a set of tools. It can then reason through tasks and take actions repeatedly until the goal is achieved.
These actions may include:
- Calling APIs
- Interacting with enterprise applications
- Updating systems or records
- Triggering operational workflows
- Analysing internal data to make decisions
In simple terms, agentic AI moves beyond answering questions to performing work inside systems.
"Agentic AI is like a junior employee with system access. It can carry out tasks independently, but it still requires oversight, clear permissions, and governance."
In the latest episode of the Cyber Insights Podcast, Ronan Murray and Ian Finlayson spoke with security leader and author Josh Woodruff about what agentic AI means for enterprise environments, and why organisations need to think about security before these systems are deployed at scale.
Why Agentic AI Changes Enterprise Security
When AI begins operating inside enterprise systems, the security model changes. Traditional applications follow predefined workflows. AI agents can reason about information and decide which actions to take next.
This means organisations need to start treating AI agents as identities operating within their environment. Like any other identity, AI systems require:
- Authentication and identity management
- Clearly defined permissions
- Monitoring of behaviour
- Segmentation of systems and data
- Governance over what they are allowed to access or change
Without these controls, organisations risk granting autonomous systems access that may expose sensitive data or critical infrastructure.
Prompt Injection: The Emerging AI Security Risk
One of the most significant risks associated with agentic AI is prompt injection.
AI agents rely heavily on the data they consume to determine their next actions. If that data contains malicious instructions, the AI system may unknowingly follow them.
Examples could include:
- Malicious instructions embedded in documents
- Manipulated data sources
- Compromised emails or tickets
- Poisoned datasets designed to influence AI behaviour
Because agentic AI operates at machine speed, the consequences of incorrect actions can occur far more quickly than traditional human-driven processes. This makes data governance and monitoring critical for organisations planning to deploy AI agents in production environments.
Why Zero Trust Matters for AI Security
Zero Trust assumes that no user, device, or workload should be trusted by default. Access must be continuously verified and limited to only what is required.
When applied to AI systems, this means:
- Every AI agent should have a unique identity
- Access should follow least-privilege principles
- Systems and data should be segmented
- Behaviour should be continuously monitored
Applying Zero Trust principles helps ensure that AI agents operate within tightly controlled boundaries, reducing the potential impact if something goes wrong.
Governing Autonomous AI Systems
The biggest risk with agentic AI is not always malicious activity. Often, it is unintended autonomy.
Organisations may deploy AI systems without fully understanding the capabilities they have granted. Without clear governance, these systems can make decisions or take actions that were never anticipated.
This is why many security experts recommend introducing AI agents gradually. Start with narrow use cases and limited access. Monitor behaviour closely and expand capabilities only as the system proves reliable.
In practice, this means treating AI agents exactly like new employees entering the organisation: restricted permissions first, with trust built over time.
For a practical framework on governing autonomous AI systems, Josh Woodruff's book Agentic AI and Zero Trust (co-authored with Michelle Savage) introduces the Agentic Trust Framework, a set of principles for safely deploying AI agents while maintaining strong identity, access, and security controls.
Preparing for the Next Phase
Agentic AI represents a significant shift in how artificial intelligence will be used inside organisations. As AI moves from experimentation into operational systems, security and governance must evolve alongside it.
Organisations that introduce strong identity controls, monitoring, and Zero Trust principles early will be far better positioned to adopt these technologies safely.
If your organisation is exploring how AI systems will interact with enterprise infrastructure, ensuring your network and security architecture is ready is essential. The Edge7 Networks team works with organisations to design secure networking and cybersecurity frameworks that support emerging technologies while maintaining strong security controls.