AI agents are quickly becoming one of the biggest talking points in business technology, which makes AI agent security a priority for every organisation.<\/p>\n
They promise to help organisations automate tasks, connect systems, reduce admin and give employees more time to focus on valuable work.<\/p>\n
That potential is real.<\/p>\n
But AI agents also change the cyber security conversation, and AI agent security needs to be considered from day one.<\/p>\n
Unlike a standard chatbot, an AI agent may be able to take action. It might search company documents, update a record, send a message, trigger a workflow or move information between business systems.<\/p>\n
That makes the security questions much more important.<\/p>\n
Because once an AI tool can access your data and act on your behalf, it can no longer be treated as a simple productivity tool.<\/p>\n
It needs governance.<\/p>\n
It needs clear boundaries.<\/p>\n
It needs proper cyber security controls.<\/p>\n
The message for businesses is not that AI agents should be avoided.<\/p>\n
The message is that they should be introduced carefully, with the right safeguards in place from the start.<\/p>\n
An AI agent is a system designed to carry out tasks using artificial intelligence. If you are new to the terminology, our guide to AI without the jargon<\/a> explains models, prompts, agents and workflows in plain English.<\/p>\n A standard AI assistant usually waits for a person to ask a question, then produces a response.<\/p>\n An AI agent can go further.<\/p>\n It can interpret a goal, decide which steps to take, retrieve information from connected sources and use software tools or APIs to complete work. That might include updating a record, sending a message or triggering a transaction.<\/p>\n For example, an AI agent could help:<\/p>\n This is why AI agents are so useful.<\/p>\n They can support work that would otherwise involve several manual steps across different platforms.<\/p>\n But that is also why the risk is different.<\/p>\n If a chatbot gives a poor answer, someone can review it before acting.<\/p>\n If an AI agent takes the wrong action, the impact may already have reached a live business system.<\/p>\n The most important difference is this:<\/p>\n AI assistants respond. AI agents can act.<\/strong><\/p>\n A conventional AI assistant usually helps with a single task.<\/p>\n It might summarise a document, draft an email or answer a question.<\/p>\n An AI agent can pursue an objective across several steps.<\/p>\n For example, an operations agent might check stock levels, compare an order against agreed rules and prepare a supplier request. A service agent could review a customer\u2019s history, identify the relevant policy and draft a response for approval.<\/p>\n That sounds helpful, and it can be.<\/p>\n But every extra step adds a new security question.<\/p>\n What information did the agent read?<\/p>\n Which system did it connect to?<\/p>\n What action did it take?<\/p>\n Was human approval required?<\/p>\n Could the same action be misused?<\/p>\n Could a mistake affect a customer, supplier or financial process?<\/p>\n These are business questions as much as technical ones.<\/p>\n AI agents become more consequential when they are connected to business systems.<\/p>\n An agent connected to nothing can only produce a response.<\/p>\n An agent connected to Microsoft 365, a CRM system, a finance platform, a ticketing tool or a customer database can do much more.<\/p>\n That means a mistake, misconfiguration or compromise may have a bigger impact.<\/p>\n If an agent misunderstands an instruction, receives misleading information or is compromised, the result may be an action taken in the business rather than an inaccurate answer waiting for someone to review.<\/p>\n That is the key point.<\/p>\n AI agents do not just create content.<\/p>\n They may affect live workflows.<\/p>\n They may touch sensitive information.<\/p>\n They may influence operational decisions.<\/p>\n That makes access, permissions, identity and monitoring essential parts of AI agent security.<\/p>\n The first question every business should ask is simple:<\/p>\n What can this AI agent access?<\/strong><\/p>\n If an agent can search company files, which files?<\/p>\n If it connects to email, whose mailbox?<\/p>\n If it uses customer records, which customers?<\/p>\n If it accesses SharePoint, Teams or OneDrive, are permissions properly controlled?<\/p>\n An agent\u2019s boundaries must reflect the systems it can reach. Access should be linked to a specific purpose, limited to the information and actions required, and reviewed when the workflow changes.<\/p>\n For example:<\/p>\n The principle is the same as any other cyber security control:<\/p>\n Give the agent only the access it needs.<\/strong><\/p>\n Nothing more.<\/p>\n Reading information is one thing.<\/p>\n Changing information is another.<\/p>\n An AI agent that can read a database does not necessarily need permission to edit it. An agent that can prepare a payment should not be able to release it without an appropriate approval step.<\/p>\n This distinction matters.<\/p>\n Many businesses already struggle with excessive user permissions, which is why access control matters<\/a> so much. AI agents can make the issue more visible because they may be able to act quickly across systems.<\/p>\n Before deployment, business leaders should ask:<\/p>\n The more impact an action could have, the stronger the control should be.<\/p>\n Some actions may be suitable for automation.<\/p>\n Others should require human approval every time.<\/p>\n Every AI agent should have a clear identity, credentials and permissions.<\/p>\n Treating it as a shared or anonymous account makes it harder to establish what happened, investigate an incident or remove access safely.<\/p>\n This is important.<\/p>\n If something goes wrong, your business needs to know:<\/p>\n A stronger design gives each agent a distinct identity, uses the minimum permissions required and limits credentials wherever possible. Logs should be clear enough to support investigation if something unexpected happens.<\/p>\n This should not be left as an afterthought.<\/p>\n Identity is one of the foundations of AI agent security and secure AI adoption.<\/p>\n Prompt injection is one of the most important AI security risks to understand. It is ranked as the top risk in the OWASP Top 10 for LLM applications<\/a>.<\/p>\n It happens when content encountered by an AI system contains instructions intended to redirect its behaviour.<\/p>\n That content might be inside:<\/p>\n To a person, the content may look like ordinary information.<\/p>\n To an AI agent, it may be interpreted as an instruction.<\/p>\n Connected content and tool responses should be treated as untrusted, even when they come from a familiar business system.<\/p>\n This matters because AI agents often rely on connected sources.<\/p>\n If an agent reads an email, document or web page containing malicious instructions, businesses need controls that stop those instructions from overriding the agent\u2019s intended purpose.<\/p>\n In simple terms:<\/p>\n Do not assume an AI agent will always know which instructions to trust.<\/strong><\/p>\n That is why testing, boundaries and human oversight are essential to AI agent security.<\/p>\n AI agents have been in the news recently because of concerns around systems acting unpredictably, following unintended instructions or taking actions beyond what people expected.<\/p>\n The takeaway should not be panic.<\/p>\n It should be preparation.<\/p>\n AI agents are powerful because they can connect to tools and complete work across systems.<\/p>\n That same power means businesses need to think carefully about permissions, approval steps, monitoring and emergency controls.<\/p>\n The goal is not to stop AI adoption.<\/p>\n The goal is to make sure the business stays in control.<\/p>\n Automation can save time, but not every action should happen automatically.<\/p>\n High-impact actions should usually require human approval.<\/p>\n That includes anything involving:<\/p>\n Effective oversight combines technical controls with human judgement. High-impact actions should require approval, unusual activity should generate an alert, and activity should be recorded well enough to support investigation.<\/p>\n AI should support people.<\/p>\n It should not quietly remove accountability from important decisions.<\/p>\n Many businesses test technology by checking whether it works in normal conditions.<\/p>\n With AI agents, that is not enough.<\/p>\n Testing should include:<\/p>\n It is particularly important to test misleading instructions, unexpected tool responses and attempts to access information outside the intended scope.<\/p>\n This is important because real-world use is rarely tidy.<\/p>\n Employees may phrase requests in unusual ways.<\/p>\n Connected systems may return unexpected information.<\/p>\n Attackers may deliberately try to manipulate behaviour.<\/p>\n Testing needs to reflect that.<\/p>\n Before an AI agent is put into production, the business should know how to stop it.<\/p>\n That means being able to:<\/p>\n An emergency shutdown route should be defined before the agent is put into production.<\/p>\n This does not need to be dramatic.<\/p>\n It simply means the business should not be working out how to stop an agent during an incident.<\/p>\n That plan should exist beforehand.<\/p>\n For many small and mid-sized businesses, AI agent adoption will connect closely with Microsoft 365.<\/p>\n Tools such as Microsoft Copilot and Copilot Studio can be powerful because they work with the systems many organisations already use every day.<\/p>\n That may include:<\/p>\n This creates a strong business case.<\/p>\n It also means your Microsoft 365 environment needs to be in good shape.<\/p>\n Before connecting AI agents to Microsoft 365, businesses should review:<\/p>\n Good AI agent security starts here, because an AI agent does not fix weak permissions.<\/p>\n It may expose them.<\/p>\n AI agents are still new for many organisations, so mistakes are understandable.<\/p>\n The most common include:<\/p>\n Businesses should begin with a clear use case, not with the newest feature.<\/p>\n Ask what problem the agent is solving and how success will be measured.<\/p>\n Broad permissions may make the agent easier to build, but they increase risk.<\/p>\n Start with the minimum access required.<\/p>\n If an action could affect customers, money, records or sensitive data, human review may still be needed.<\/p>\n Every agent needs an owner responsible for its purpose, permissions, performance and retirement.<\/p>\n A safe setup can become risky if systems, users, permissions or workflows change.<\/p>\n AI agents are part of your wider technology environment.<\/p>\n They need to fit into your identity, access, data protection, monitoring and incident response arrangements.<\/p>\n Before rolling out an AI agent, business leaders should be able to answer:<\/p>\n If these answers are unclear, the business is probably not ready to deploy the agent widely.<\/p>\n That does not mean abandoning the idea.<\/p>\n It means tightening the setup first.<\/p>\n AI agents can create real value for businesses, but they need to be introduced safely.<\/p>\n At SOD-IT, we help organisations prepare for AI adoption with a practical focus on security, governance and usability.<\/p>\n That may include:<\/p>\n We help businesses understand what AI can do, where it can add value and what controls should be in place before it becomes part of daily operations.<\/p>\n The aim is not to make AI complicated.<\/p>\n It is to make AI safe enough to be useful.<\/p>\n AI agents represent an exciting shift in how businesses can use technology.<\/p>\n They can reduce admin, connect systems and help teams work more efficiently.<\/p>\n But once AI can act, businesses need to think carefully about access, permissions, identity, data and accountability.<\/p>\n The organisations that benefit most will be the ones that adopt AI with confidence rather than panic.<\/p>\n That means starting with clear use cases, setting sensible boundaries and making sure AI agent security is built in from the beginning.<\/p>\n If your business is considering Microsoft Copilot Studio, AI agents or wider AI adoption, get in touch with SOD-IT<\/a>. We can help you assess your current setup and build a secure rollout plan.<\/p>\n \ud83d\udcde 0141 488 1533<\/strong>\n
\n<\/div>\nFrom Assistance to Action<\/h2>\n
\n<\/div>\nAI Agent Security: Why Agents Create New Challenges<\/h2>\n
\n<\/div>\nAI Agent Security Starts With Access: What Can the Agent See?<\/h2>\n
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\n<\/div>\nWhat Can the Agent Change?<\/h2>\n
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\n<\/div>\nIdentity Is Central to AI Agent Security<\/h2>\n
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\n<\/div>\nPrompt Injection: An AI Agent Security Risk to Understand<\/h2>\n
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\n<\/div>\nWhy Recent \u201cAI Agent Going Rogue\u201d Stories Matter<\/h2>\n
\n<\/div>\nHuman Approval Still Matters<\/h2>\n
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\n<\/div>\nAI Agent Security Testing Should Go Beyond Normal Use<\/h2>\n
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\n<\/div>\nEvery Agent Needs an Emergency Stop<\/h2>\n
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\n<\/div>\nAI Agent Security in Microsoft 365<\/h2>\n
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\n<\/div>\nCommon Mistakes Businesses Should Avoid<\/h2>\n
Starting with the tool instead of the task<\/h3>\n
Giving too much access<\/h3>\n
Skipping human approval<\/h3>\n
Forgetting ownership<\/h3>\n
Not reviewing over time<\/h3>\n
Treating AI as separate from cyber security<\/h3>\n
\n<\/div>\nPractical Questions to Ask Before Deploying an AI Agent<\/h2>\n
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\n<\/div>\nHow SOD-IT Can Help With AI Agent Security<\/h2>\n
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\n<\/div>\nAI Agents Are Powerful. Make Sure Your Business Stays in Control.<\/h2>\n
\n\ud83d\udce7 support@sod-it.co.uk<\/strong><\/a>
\n\ud83c\udf10 www.sod-it.co.uk<\/strong><\/a><\/p>\n","protected":false},"excerpt":{"rendered":"