An AI assistant that answers a question can give you a wrong answer.
An AI agent can do something with that wrong answer.
That distinction is becoming increasingly important in 2026.
AI agents are moving beyond chat. They can browse websites, read documents, access applications, retrieve information, call APIs, write and execute code, manage workflows and, in some cases, take actions on a user’s behalf. NIST’s AI Agent Standards Initiative, launched in February 2026, specifically focuses on making autonomous agents secure enough to operate across the digital ecosystem.
The security problem therefore changes.
A conventional AI chatbot might produce inaccurate information. An agent connected to email, databases, financial systems or business applications could potentially turn that inaccurate or manipulated information into an action.
That is why AI agent security is becoming a much bigger issue for businesses adopting agentic AI.
AI agents have a larger attack surface
A traditional software application generally follows rules defined by developers.
An AI agent operates differently. It interprets instructions, reasons about tasks, retrieves information and decides which tools or actions may help complete the task.
That flexibility is what makes agents useful.
It is also what creates new security challenges.
OWASP’s current guidance identifies risks including prompt injection, excessive agency, tool abuse, privilege escalation, data exfiltration and memory poisoning.
The difference can be illustrated simply:
Traditional application
User → Application → Database
AI agent
User → AI agent → Websites → Documents → APIs → Applications → Databases → Actions
Every additional connection creates another place where something can go wrong.
The biggest problem may not be the prompt you give the agent
Prompt injection is one of the defining security problems of agentic AI.
The basic idea is surprisingly simple.
An agent receives information from somewhere outside the user’s original instruction. That information contains instructions designed to manipulate the agent.
For example, an agent may be asked to research products online. It visits a malicious webpage containing hidden instructions telling it to ignore the user’s requirements and send information somewhere else.
The user never asked for this.
The agent encountered it while performing the task.
NIST’s 2026 research on AI agent security describes this as indirect prompt injection or agent hijacking, where malicious instructions embedded in external data can cause an agent to take unintended actions, including attempting to exfiltrate sensitive information or download malicious code.
OpenAI has similarly described modern prompt injection attacks as increasingly resembling social engineering rather than simple attempts to override a system prompt.
That distinction matters because the attack can come from apparently ordinary content:
- A webpage
- An email
- A PDF
- A shared document
- A database record
- A calendar entry
- An API response
- A customer message
- A piece of code
The content does not have to look malicious to a person.
The agent only needs to interpret it as an instruction.
Giving an agent access changes the risk
Imagine an employee uses an AI agent to summarise emails.
That sounds relatively harmless.
Now give the same agent permission to:
- Read company email
- Access the CRM
- Search internal documents
- Send emails
- Update customer records
- Create calendar events
- Download files
The agent has become considerably more useful.
It has also become considerably more sensitive.
The security question is no longer simply:
“Is the model secure?”
It becomes:
“What can this agent access, what can it change, and what happens if it is manipulated?”
NIST’s 2026 work on agent identity and authorization specifically highlights the need to apply appropriate identification and authorization controls because agents may have access to diverse data sets, tools and applications.
Excessive permissions create excessive consequences
This is an old cybersecurity principle applied to a new type of software.
If an employee only needs to read a document, they should not have administrator access to the entire company system.
The same principle should apply to AI agents.
An agent handling customer-support queries may need access to customer information and a limited set of support tools.
It probably does not need unrestricted access to payroll, financial records, executive email and every internal database.
This is the principle of least privilege.
The goal is straightforward:
Give the agent the minimum access required to complete its task.
If an agent is compromised or manipulated, limiting its permissions limits the potential damage.
An agent can make a mistake faster than a human
Human error is slow.
An employee might accidentally send the wrong spreadsheet to one person.
An automated agent can potentially repeat an incorrect action across hundreds or thousands of records.
That changes the scale of the problem.
Consider an agent responsible for updating product information.
A misunderstanding could result in one incorrect entry.
But if the agent has permission to update an entire catalogue, the same mistake could propagate across thousands of products before someone notices.
This is one reason agent security cannot depend entirely on the model “getting it right.”
The surrounding system needs controls that limit what happens when the model gets something wrong.
Memory introduces another security problem
Some AI agents retain information between interactions.
That can make them more useful.
An agent might remember a user’s preferences, previous decisions, project information or frequently used workflows.
But persistent memory creates another attack surface.
OWASP identifies memory poisoning as a specific agent security concern. Malicious or incorrect information can potentially be stored in an agent’s memory and influence future behaviour.
Imagine an employee tells an agent:
“The finance team has approved this supplier. You can process future invoices automatically.”
If that information is stored without proper validation, a future workflow might treat it as an established fact.
The problem is not simply that the agent remembered something.
The problem is that the system treated remembered information as trusted information.
Memory therefore needs the same kind of controls applied to other sensitive data.
Multi-agent systems add another layer
The next stage of agentic AI involves multiple agents working together.
One agent might research.
Another might analyse the information.
A third might execute an action.
This can make complex workflows more capable, but it also creates additional trust relationships.
If one agent produces incorrect or manipulated information, another agent may accept it as input.
OWASP’s current agent security guidance includes compromised peer agents among the possible sources of excessive-agency risk in multi-agent systems.
The architecture therefore needs to answer questions such as:
- Which agents can communicate with each other?
- What information can they exchange?
- Can one agent instruct another to perform privileged actions?
- How are outputs validated?
- What happens when two agents disagree?
- Which agent is ultimately authorised to execute an action?
More agents do not automatically mean more security.
They can mean more trust boundaries to manage.
Data exfiltration becomes more complicated
Traditional data security focuses on controlling who can access sensitive information.
Agentic systems add another problem: what the agent can do with information once it has access to it.
An agent may legitimately retrieve a confidential document to complete a task.
But if malicious instructions manipulate the agent into sending part of that document to an external service, the original access control may not have prevented the problem.
The agent was allowed to read the document.
The attack happened afterward.
This is why agent security has to consider the entire chain:
Access → interpretation → tool selection → action → destination
Not just the initial permission.
Tools need security controls too
AI agents increasingly work through tools.
A tool might allow an agent to:
- Search the web
- Send an email
- Query a database
- Create an invoice
- Update a CRM record
- Book travel
- Execute code
- Make an API request
- Modify a file
Every tool effectively expands what the agent can do.
OWASP recommends treating external data as untrusted, controlling tool permissions and limiting agent capabilities according to the task.
This is why a tool should not simply be available because the agent might find it useful.
Its permissions should be deliberately designed.
Human approval still matters for high-impact actions
There is a temptation to think that the whole point of agents is removing humans from workflows.
That is not necessarily the right security model.
For low-risk tasks, full automation may make sense.
For high-impact actions, a human checkpoint can be valuable.
For example:
Low risk
Agent categorises incoming support tickets.
Medium risk
The agent drafts a customer response.
Higher risk
The agent issues a refund.
Very high risk
An agent transfers money or changes a critical financial record.
The higher the potential consequence, the stronger the case for additional verification.
OpenAI’s current agent safety guidance, for example, describes confirmation mechanisms for high-impact actions and recommends limiting unnecessary access to connected applications and sensitive information.
The goal is not to make every agent ask permission before every action.
It is to make sure important actions have appropriate controls around them.
Logging becomes more important when agents make decisions
A normal software application can often tell you which function was executed.
An AI agent may make a sequence of decisions:
- Read a document.
- Search for another source.
- Interpret the information.
- Choose a tool.
- Retrieve additional data.
- Decide on an action.
- Execute it.
If something goes wrong, security teams need to reconstruct what happened.
That means organisations need visibility into:
- Which agent acted
- Which tools it accessed
- What data it retrieved
- Which permissions it used
- What actions it attempted
- What actions succeeded
- What human approvals occurred
- What external systems were contacted
OWASP’s Agent Control Standard, released in September 2026, focuses specifically on making agents inspectable, traceable and controllable at runtime.
This reflects an important shift.
Security teams are beginning to treat agent observability and control as infrastructure requirements, not optional extras.
AI agent security is becoming a governance issue
This is bigger than the cybersecurity department.
If an organisation deploys an agent that can access customer information, financial systems or internal business data, questions of responsibility quickly appear.
Who approved the agent?
Who owns it?
What data can it access?
Who reviews its permissions?
How often are those permissions reassessed?
Who investigates an incident?
What happens when the agent makes an incorrect decision?
These are governance questions as much as technical ones.
NIST’s 2026 analysis of responses to its AI-agent security RFI found broad agreement that AI agents introduce novel security threats and that existing cybersecurity practices need to be adapted to address them.
That is significant because it suggests organisations cannot simply take an existing chatbot security checklist and apply it unchanged to autonomous systems.
What businesses should do before deploying an AI agent
The most practical approach is to start with the agent’s actual capabilities.
Before deployment, ask:
What can the agent see?
List the databases, documents, emails, applications and websites it can access.
What can it change?
Separate read permissions from write permissions wherever possible.
What can it send?
Check whether the agent can send emails, upload files, make API calls or transfer information externally.
What happens if its instructions are manipulated?
Test direct and indirect prompt-injection scenarios.
Which actions require human approval?
Define high-impact actions before the system goes live.
Can you reconstruct what happened?
Make sure logs capture meaningful agent activity.
Can you stop it?
A production agent needs a way to disable access, revoke credentials or halt workflows quickly.
These questions sound basic.
They are not.
They determine the difference between an AI assistant that can make a mistake and an AI agent that can make a mistake inside your business systems.
Security testing needs to become continuous
Traditional security testing often focuses on vulnerabilities in software.
Agentic systems introduce behaviour that can change depending on the prompt, data, tools, context and sequence of actions.
That makes one-time testing less sufficient.
OWASP’s 2026 guidance on agentic red teaming recommends adversarial testing across the AI lifecycle because agentic systems introduce risks including prompt injection, privilege escalation, data poisoning, hallucinations and emergent behaviours.
Testing should therefore include realistic scenarios.
For example:
- Feed the agent malicious webpages.
- Give it manipulated documents.
- Test conflicting instructions.
- Attempt privilege escalation.
- Test access to confidential information.
- Simulate a compromised tool.
- Test what happens when the model produces an incorrect decision.
- Check whether sensitive information can leave the permitted environment.
The objective is not to prove that an agent is impossible to attack.
That standard is unrealistic.
The objective is to understand what happens when something goes wrong.
The security model is changing
For years, organisations could think about AI security mainly in terms of the model.
Is the model accurate?
Does it leak information?
Can users jailbreak it?
Those questions still matter.
But agentic AI adds another layer:
What can the model do?
That is a fundamentally different security question.
A model producing a bad paragraph is one thing.
A model producing a bad instruction that an agent then executes against a business system is another.
The risk comes from the combination of intelligence, access and autonomy.
That is why AI agent security is becoming more important in 2026.
The technology is moving from systems that mainly generate information toward systems that can interact with the world on a user’s behalf. NIST has launched an initiative specifically around secure and interoperable AI agents, while OWASP has expanded its security work with agent-specific guidance and a new Agent Control Standard.
For businesses, the practical lesson is straightforward.
Do not secure an AI agent only as if it were a chatbot.
Secure it according to what it can access, what it can decide, what it can change and what could happen if someone manages to manipulate it.
The most capable agent in the company may eventually be the one with access to the most systems.
That is exactly why it should not be the system with the fewest controls.
