What Is Agentic AI, and Why Does It Matter for Cybersecurity?

Artificial intelligence is moving from the chat window into the workplace.

For the past few years, much of the conversation around AI has focused on content generation: tools that can write emails, summarise documents, draft code, produce images or answer questions. But a newer phase is beginning to take shape. One where AI does not simply respond to a prompt, but plans, decides and acts.

This is the world of agentic AI.

The UK’s National Cyber Security Centre describes agentic AI systems as tools that can access data sources, remember context, make decisions, use tools and take actions in pursuit of a goal. Unlike a standard chatbot, an AI agent may be able to interact with software, connect to business systems, trigger workflows, create sub-agents and operate without continuous human supervision. That makes the technology potentially powerful, but also significantly more difficult to control (NCSC – Thinking carefully before adopting agentic AI; MIT Sloan, Agentic AI, Explained).

For businesses, the question is no longer simply whether AI can improve productivity. It is whether organisations understand what they are allowing these systems to access, what decisions they are permitted to make, and what could happen if something goes wrong.

From assistant to actor

The difference between generative AI and agentic AI is subtle, but important. A generative AI tool might help an employee draft a report. An agentic AI system could, in theory, go several steps further: gathering source data, analysing it, preparing the report, emailing stakeholders, updating a CRM record and scheduling a follow-up.

That shift from suggesting to doing is where the cybersecurity implications begin.

The NCSC has warned that many of the risks around agentic AI are familiar: access control, secure development, supply chain risk, monitoring, incident response and accountability. But the added autonomy of these systems expands the attack surface and makes behaviour harder to predict, test and govern (NCSC – Thinking carefully before adopting agentic AI).

In other words, agentic AI does not replace traditional cyber risk. It intensifies it.

If an AI agent is connected to email, file storage, customer records or internal systems, it becomes more than a productivity tool. It becomes a new digital actor inside the organisation. It may have permissions. It may hold context. It may make decisions quickly. And, if compromised or manipulated, it may be able to cause damage at machine speed.

The new risk is autonomy

Cybersecurity teams are used to managing users, devices, applications and identities. Agentic AI complicates that model because it introduces systems that can behave with a degree of independence.

That independence creates several uncomfortable questions.

• Who approved the agent’s access?
• Who monitors what it does?
• Who is accountable if it makes a harmful decision?
• Can its actions be reversed?
• Can the organisation explain why it acted in a particular way?

These are not theoretical concerns. In May 2026, joint guidance from CISA, the UK NCSC and international cyber agencies warned that agentic AI introduces security risks beyond those associated with traditional software or GenAI.

The guidance urged organisations to adopt agentic AI carefully, keep early deployments to low-risk and non-sensitive tasks, and avoid giving AI agents broad access to sensitive data or critical systems (CISA, Careful Adoption of Agentic AI Services; NCSC, Thinking Carefully Before Adopting Agentic AI).

In other words, agentic AI is already being treated by leading cyber agencies as a live governance and security issue, not a distant future concern.

Prompt injection becomes more dangerous when AI can act

One of the biggest concerns around agentic AI is that it inherits known large language model risks, including susceptibility to prompt injection and jailbreaking (NCSC – Thinking carefully before adopting agentic AI).

Prompt injection is not just a technical curiosity. OWASP describes it as a way of manipulating an AI system through specific inputs that alter the model’s behaviour. In practice, that can include malicious instructions hidden inside content the model reads, such as an email, document or webpage (OWASP GenAI Security Project, LLM01:2025 Prompt Injection).

For a chatbot, the result may be a misleading or unsafe response. For an AI agent connected to business systems, the consequences could be more serious. If an agent were asked to review supplier emails, a malicious message could potentially influence how it behaves, especially if the agent has permission to access data, trigger workflows or take action.

That is why prompt injection becomes more concerning in agentic AI. OWASP notes that modern LLMs and GenAI have expanded the scale, capabilities and associated risks of autonomous systems. Its prompt injection guidance also warns that successful attacks can lead to unauthorised access to functions, arbitrary commands in connected systems and manipulation of critical decision-making processes (OWASP GenAI Security Project, Agentic AI: Threats and Mitigations; OWASP GenAI Security Project, LLM01:2025 Prompt Injection).

The issue is not that AI agents are inherently malicious. It is that they may struggle to distinguish between legitimate instructions, malicious content and unexpected context. When those agents are given tools and permissions, that confusion can become a security problem.

Governance is becoming the dividing line

As with many emerging technologies, the organisations most at risk may not be those experimenting with agentic AI, but those doing so without clear governance.

Gartner warned in May 2026 that applying the same governance approach to every AI agent can lead to enterprise AI agent failure. The firm predicts that by 2027, 40% of enterprises will demote or decommission autonomous AI agents because governance gaps are only discovered after production incidents occur (Gartner, Applying Uniform Governance Across AI Agents Will Lead to Enterprise AI Agent Failure).

The central problem is that not all AI agents carry the same level of risk. An agent that summarises documents using read-only access is very different from one that can modify system configurations, send emails, approve workflows or interact with customer data.

Gartner’s model separates agents by autonomy level: observe, advise, act with approval and act autonomously. That distinction matters because controls should increase as autonomy increases. A read-only assistant may require lighter controls, such as scoped access, testing and logging. An autonomous agent operating across business systems requires much stronger oversight, monitoring and accountability (Gartner, Applying Uniform Governance Across AI Agents Will Lead to Enterprise AI Agent Failure).

For businesses, this means agentic AI cannot be governed with a simple “allow” or “block” mindset. It requires a risk-based approach.

Why attackers will care about AI agents

Cybercriminals tend to follow access, scale and opportunity. Agentic AI offers all three.

If agents become embedded into business workflows, they may become attractive targets for attackers looking to exploit trust. A compromised employee account is already dangerous. A compromised or manipulated AI agent with access to multiple systems could be worse.

The World Economic Forum’s Global Cybersecurity Outlook 2026 warns that accelerating AI adoption is reshaping the global risk landscape, while attacks are becoming faster, more complex and more unevenly distributed (World Economic Forum – Global Cybersecurity Outlook 2026).

Agentic AI fits squarely into that trend. For defenders, it could help automate repetitive security tasks, triage alerts, investigate incidents and speed up response. Microsoft and Google Cloud are already positioning AI agents around these kinds of security operations use cases. But similar automation could also be useful to attackers, particularly for scaling reconnaissance, improving social engineering and accelerating the exploitation of known vulnerabilities (Microsoft, Security Copilot Agents; Google Cloud, Agentic SOC; NCSC, Impact of AI on Cyber Threat from Now to 2027).

This is the double-edged nature of the technology. The same automation that helps a security team investigate faster could help a threat actor move faster too.

The cybersecurity opportunity

Despite the risks, agentic AI should not be viewed only as a threat.

Used carefully, it could support cyber defence in meaningful ways. Microsoft, for example, has already announced Security Copilot agents designed to assist with areas such as phishing, data security and identity management, arguing that the pace and complexity of cyberattacks has exceeded what human teams can manage alone (Microsoft).

Google Cloud has also described multi-agent AI systems that could help orchestrate security operations workflows across SIEM, threat intelligence, cloud security posture management and endpoint detection tools (Google - Agentic AI use case: Orchestrate security operations workflows).

For stretched security teams, this could be valuable. AI agents may help reduce alert fatigue, speed up investigation, connect signals across tools and automate routine actions.

But the opportunity depends on control. An AI agent used in a security operations centre must be secured like any other powerful system. It needs clear scope, strong identity controls, audit trails, human oversight and incident response planning.

The technology may assist defenders, but it does not remove the need for governance. If anything, it makes governance more important.

What businesses should do now

For most organisations, the immediate priority is not to rush into autonomous AI. It is to understand where AI is already being used and what level of access it has.

That means asking practical questions before deployment:

• What task is the agent meant to perform?
• Does it actually need autonomy, or would a simpler automation work?
• What data can it access?
• Can it write, delete, send, approve or modify anything?
• Is a human required to approve important actions?
• Are its actions logged and monitored?
• Can access be revoked quickly?
• Who is accountable if something goes wrong?

The NCSC’s advice is clear: start small, use agents only for tightly bounded tasks, apply least privilege, avoid long-lived credentials, monitor behaviour and plan for incidents before connecting agentic AI to real systems or sensitive data (NCSC – Thinking carefully before adopting agentic AI).

That is sensible advice for any business. Especially SMEs, where new tools can often be adopted informally before IT, security or leadership teams have had a chance to assess the risk.

The bigger issue: trust

Agentic AI forces organisations to confront a difficult question: how much trust should be given to a machine that can act?

The answer should not be blind trust, but controlled trust.

AI agents should be treated as non-human identities inside the organisation. They need defined permissions, clear boundaries, monitoring, testing and ownership. They should not be granted broad access simply because they are useful, new or embedded into a familiar platform.

The organisations that benefit most from agentic AI will likely be those that adopt it deliberately. They will understand the difference between low-risk assistance and high-risk autonomy. They will involve cybersecurity teams early and will document decisions, test failure scenarios and make sure humans remain accountable.

The organisations that struggle may be those that allow agentic AI to spread quietly through everyday tools, workflows and departments without knowing what has been connected, what data is exposed or who is responsible.

Final thoughts

Agentic AI marks a significant shift in the way businesses use artificial intelligence. It moves AI from a passive assistant to an active participant in digital operations.

That shift brings opportunity. It also brings risk.

For cybersecurity, the concern is not simply that attackers may use AI. It is that businesses may begin giving AI agents access to systems, data and workflows before they have the governance to manage them safely.

Agentic AI may become a valuable tool for defenders. But without clear controls, it could also become another route into the organisation.

The message for business leaders is simple: do not wait until an AI agent makes a mistake to decide who is responsible for it.

Exploring how AI could impact your organisation’s cyber risk? TechForce Cyber can help you assess emerging threats, strengthen governance, and build practical controls around new technologies. Speak to our team today.