Agentic AI is moving fast from buzzword to business reality. Employees are no longer just “using” AI; they are starting to delegate to it. From office staff wiring up AI agents to automate reporting, right through to engineers building multi-agent systems that can reason, plan, and act across tools and environments, the risk surface is expanding in ways traditional security policies were not designed for.

This article explores what secure and ethical behaviour looks like in an era of agentic AI, how human and AI behaviours intersect, and what you can do to promote safe, responsible use across your organisation. The focus is practical: helping you support innovation while keeping security, safety, and ethics firmly in view.

What do we mean by “Agentic AI”?

Traditional AI tools are largely reactive. You ask a question, they respond. Agentic AI goes further. It can:

  • Break goals into sub-tasks

  • Call tools and systems on your behalf

  • Take actions, not just generate content

  • Work over longer time horizons without constant human prompting

Think of an employee asking an AI agent to “prepare and send monthly performance reports to all regional managers,” or a developer designing a multi-agent solution where one agent pulls data, another analyses it, and another drafts recommendations and updates dashboards.

This is powerful, but it also means your organisation is effectively giving software the power to move, transform, and sometimes transmit data and instructions at scale. That requires a different mindset around secure and ethical behaviour.

Where human and AI behaviour meet

Agentic AI does not exist in a vacuum. It reflects and amplifies the behaviour, intent, and blind spots of the people who design, configure, and deploy it.

Some key intersections between human and AI behaviour:

  1. Goal setting and incentives
    Humans specify the goals. If the goal is vague (“make this process faster”) or narrow (“reduce time to close tickets”) without constraints around security, privacy, and fairness, the AI will optimise for speed, not safety. That mirrors human behaviour: what gets measured gets done.

  2. Mental models and assumptions
    Employees may see AI as a smart assistant that “knows what it’s doing,” when in reality it is following patterns and instructions without real understanding or values. Over-trust, automation bias, and a tendency to skip checks are all human behaviours that can be amplified by agentic tools.

  3. Shortcuts and workarounds
    If people are under pressure to deliver, they may grant excessive permissions, hard-code credentials, or bypass controls “just to get the agent working”. The agent then operates within that over-permissive environment, turning local shortcuts into systemic risks.

  4. Social norms and culture
    If the culture treats AI experimentation as a grey area, people will quietly build and deploy agents without involving security, data protection, or ethics teams. Shadow IT becomes shadow AI, and you lose visibility of behaviour at exactly the point it becomes most powerful.

The behaviour of AI agents is, in practice, an extension of human behaviour and organisational culture. So securing agentic AI starts with influencing how your people think, decide, and act.

Key risks with agentic AI in everyday business use

Whether it is an office worker wiring up workflows in a no-code platform or a developer building a multi-agent orchestration layer, many of the underlying risks are the same. The difference lies in scale, impact, and complexity.

1. Data leakage and privacy risk

Agents often need access to data to be useful. The danger arises when:

  • Sensitive client or personal data is copied into prompts or training data

  • Agents are connected to data sources far broader than needed

  • Outputs are stored, reused, or shared beyond their original context

In a manual world, a single employee might mishandle a spreadsheet. In an agentic world, a misconfigured agent could systematically exfiltrate or expose entire datasets through logs, third-party services, or unsafe outputs.

2. Excessive permissions and lateral movement

To perform tasks, agents may need access to email, document stores, ticketing systems, code repositories, or CI/CD pipelines. If access is granted at too high a level “for convenience,” the agent becomes a high-value target.

A compromised agent configuration or credential could allow an attacker to:

  • Read or alter sensitive documents

  • Send messages that appear legitimate

  • Modify code or configuration

  • Create or approve changes in production pipelines

What used to require multiple compromised accounts can now be achieved by compromising one over-privileged agent identity.

3. Integrity and reliability of outputs

AI agents can hallucinate, misinterpret instructions, or apply rules inconsistently. When they are only suggesting content, this is inconvenient. When they are making decisions, updating records, or taking actions automatically, errors become far more serious.

Examples include:

  • Incorrect financial entries made at scale

  • Misrouted or inappropriate communications to customers

  • Misconfigured access controls or firewall rules

  • Faulty analysis informing strategic decisions

Without proper validation, agents can introduce subtle, systemic corruption into your data and processes.

4. Security vulnerabilities in agentic workflows

Developers building multi-agent systems face additional risks:

  • Insecure tool integrations or plugins

  • Prompt injection vulnerabilities in systems that read untrusted input

  • Inadequate handling of authentication, authorisation, and secrets

  • Insufficient logging and monitoring of agent actions

If agents consume external content (emails, tickets, web pages, user messages), that content can be crafted to manipulate the agent’s instructions, bypass safeguards, or trigger harmful actions.

5. Ethical and societal concerns

Beyond security, there are wider ethical issues:

  • Agents used to monitor staff in intrusive ways

  • Biased decision-making amplified by automated workflows

  • Lack of transparency about when decisions are AI-driven

  • Automation that removes human oversight from sensitive decisions

An organisation that embraces agentic AI purely for efficiency risks eroding trust, damaging its reputation, and falling foul of emerging regulation.

Office employees: safe experimentation with AI agents

Non-technical employees are often among the first to experiment with agentic capabilities, particularly in productivity suites and workflow builders. They are typically motivated by good intentions: saving time, improving processes, reducing manual work.

To keep this safe, you need to provide clear behavioural guardrails and psychological safety so staff feel able to ask for help.

Practical expectations to set for office employees:

  • Treat AI agents as junior assistants, not experts. They need oversight.

  • Never connect an agent to systems or data unless you understand what access it truly needs, and why.

  • Do not paste or stream highly sensitive data (such as payroll, health data, or confidential legal documents) into prompts or workflows without explicit approval.

  • Keep humans in the loop for decisions that affect people’s rights, finances, or employment.

  • Log what your agent is doing and keep records of which systems it can access.

Encourage staff to bring their ideas to a central team that can help assess risk, rather than pushing them to “go figure it out alone”.

Developers: secure-by-design for multi-agent systems

For engineers and product teams, agentic AI opens up new patterns: task-orchestrating agents, tool-using agents, collaborative agents with specialised roles, and so on. The development challenge is to retain strong engineering discipline while working with stochastic systems.

Principles for developers to follow:

  • Apply least privilege to agent identities and API keys. An agent should only see and do what is essential for its role.

  • Separate environments for experimentation, testing, and production. Do not allow experimental agents to touch live customer data.

  • Design for secure failure. Assume prompts may be manipulated, tools might return unexpected responses, and agents may hallucinate. Build checks, guardrails, and safe defaults.

  • Implement human approval gates for high-impact actions such as financial transactions, changes to access control, or external communications.

  • Monitor and log all agent actions to support investigation, root-cause analysis, and continuous improvement.

Your secure development lifecycle needs to adapt to recognise agent logic, prompt handling, tool integrations, and policy enforcement as first-class security concerns.

Human-centric governance: shaping behaviour, not just writing policies

A purely technical response to agentic AI will fail. You can deploy controls, but if people do not understand them, believe in them, or see them as legitimate, they will find ways around them.

Instead, think in terms of behaviour: what do you want people to actually do when they design, configure, or use AI agents?

Four angles to focus on:

  1. Capability
    Build baseline literacy on what agentic AI is, what it can and cannot do, and why security and ethics matter. This includes training on data sensitivity, prompt injection, over-trust, and human-in-the-loop patterns, tailored for different roles.

  2. Opportunity
    Provide safe, supported environments for experimentation. Sandboxes, approved tools, and clear routes to escalate promising ideas reduce the temptation to spin up unapproved agents on external platforms.

  3. Motivation
    Frame secure and ethical behaviour as part of professional pride and organisational values, not an obstacle to getting work done. Share stories where careful design prevented incidents, and recognise teams who build responsibly, not just quickly.

  4. Social norms
    Encourage leaders and early adopters to model good behaviour: documenting their agents, involving security and data protection colleagues early, and being transparent about limitations and risks.

When people believe that “this is how we do AI here,” safe practices become habitual rather than forced.

Tips to reduce security, safety, and ethical risks

The exact controls you implement will depend on your industry, risk appetite, and maturity. However, there are some practical steps that work across most organisations.

Keep bullets to a minimum, but here a concise list is helpful:

  • Define clear guidelines on acceptable use of agentic AI, including what data can be used, which systems agents may access, and when human approval is required.

  • Introduce a simple intake and review process for new AI agent ideas, so employees know where to go and feel supported, not blocked.

  • Classify data and align agent permissions to that classification, ensuring highly sensitive data is never accessed or processed by experimental agents.

  • Implement role-based access control and least privilege for all agent identities, with regular reviews and revocation.

  • Use secure credential management for agent access to tools and systems; never embed secrets in prompts, configs, or code repositories.

  • Train employees on recognising and defending against prompt injection and manipulation, especially where agents consume external or user-generated content.

  • Require human-in-the-loop checks for high-risk tasks and decisions, and log those approvals.

  • Monitor and audit agent activity, including prompts, tool calls, and resulting changes, so you can detect anomalies and learn from near misses.

  • Establish an escalation path and a blameless reporting culture for AI-related incidents, experiments gone wrong, or near misses.

These measures should be implemented with proportionality in mind. Overly heavy controls will simply drive shadow AI use; the goal is to make the safe path the easiest path.

Building a culture of CyberWise AI use

Ultimately, secure and ethical behaviour with agentic AI is a culture question. Technology evolves, but the underlying patterns of human behaviour do not change overnight.

To build a CyberWise culture around AI agents:

  • Talk openly about both the opportunity and the risk. Over-hyping benefits while glossing over downsides encourages reckless behaviour.

  • Involve a cross-functional group in your AI governance: security, data protection, legal, ethics, HR, product, and frontline teams.

  • Encourage experimentation, but within boundaries, and celebrate teams who pause to ask, “Should we?”, not just “Can we?”.

  • Make security and ethics part of design reviews, retrospectives, and success criteria for AI-enabled projects.

  • Provide clear, human-centred documentation and checklists so staff can act safely without needing to be AI experts.

Agentic AI will amplify whatever behaviour and norms already exist inside your organisation. If your culture prizes speed above all else, you will get fast, fragile, and potentially unsafe automation. If you invest in secure, ethical, and thoughtful experimentation, you will get responsible innovation that strengthens trust with your customers, regulators, and employees.

The choice is not between embracing agentic AI or avoiding it. The real choice is whether you shape the behaviour around it, or let it evolve in the shadows.