Autonomy is the feature. And the attack surface.
One obsession: letting your AI agents act on their own without letting them act against you. The very autonomy that makes an agent useful is what makes it dangerous the moment nobody is watching.
We secure autonomous agents the way we run them: least privilege, hard guardrails, a senior human on the high-stakes calls, and a kill switch that always works.
Not application security with a new logo.
An agent is not a program that does the same thing every time, and it is not a person you can train once. It reads untrusted language, holds real privileges, and decides its own next step. That breaks most of the assumptions your current stack was built on.
The insider is now a machine
Every agent is a new non-human identity with delegated access, and these identities already outnumber people many times over. Give one too much standing privilege and a single hijack can move across your data and tools at machine speed, long before a human notices.
The lethal trifecta
Risk spikes when an agent can touch three things at once: private data, untrusted content and a way to send data out. Combine them without controls, and a hidden line on a single web page can turn a helpful agent into a quiet leak.
Non-deterministic by nature
An agent is one of the first kinds of software whose output is not deterministic. The same prompt can return a different answer twice, so you cannot certify it once and walk away. You observe its behaviour continuously, because correct today does not guarantee correct tomorrow.
Agentic security is the safety layer under everything autonomous. It pairs with Process Automation & Agentic AI, which builds the agents, AI Enablement, which deploys AI across the business, and Cloud Services, where much of it runs.
The first big agentic breach will not be a hack. It will be a helpful agent.
Autonomy changed the shape of the risk. These are the three reasons it belongs on the board agenda now, not after the incident.
Mistakes at machine scale
An agent can act thousands of times before a human looks up. One wrong turn, or one hijack, scales instantly, so the damage is done before anyone reacts.
The rules arrived first
The EU AI Act, DORA and sector rules already expect governance, oversight and reporting on autonomous systems. Running them uncontrolled is now a compliance exposure, not just a technical one.
Pilots die unsecured
Most agent projects stall before production because nobody could make them safe enough to trust. Security is not the brake on agentic value. It is the thing that lets it ship.
The upside of autonomy is real, and so is the downside. The job is to capture the first without inheriting the second, which is the entire point of this practice.
More machine identities, more agents, and not enough defenders.
Autonomy is scaling faster than the security around it. The numbers below are reported industry benchmarks, they move over time, but the direction is not in doubt.
Machine identities for every human, the pool an agent's credentials come from (CyberArk, 2025).
of enterprise apps include AI agents (Gartner).
the global cybersecurity workforce gap, so defenders are already stretched thin (ISC2, 2024).
of organizations have no identity security controls for their AI (CyberArk, 2025).
Safeguards are improving, but the models keep getting smarter and companies keep delegating more. When capability and delegation outrun the security around them, the exposure is the gap itself. Half of security leaders have already had a breach tied to a compromised machine identity, which is exactly why agentic systems deserve their own security discipline. Figures are reported industry benchmarks and change over time.
Ten ways an agent goes wrong. We plan for all ten.
The OWASP Top 10 for Agentic Applications is the shared map of how autonomous systems fail. These are the ones that cost real money, and the ones we design against from day one.
Goal hijacking
Hidden instructions quietly rewrite what the agent is trying to do, so it works for an attacker while looking like it is doing its job.
Tool misuse
The agent is tricked into using its real tools for the wrong ends, from mass data reads to actions it was never meant to take.
Identity & privilege abuse
Shared or over-broad credentials let a hijacked agent reach well beyond its task. Least privilege and per-agent identity shut that down.
Agentic supply chain
Malicious tools, servers and packages slip in through what an agent depends on. We curate, pin and verify everything it can use.
Unexpected code execution
An agent with a code or shell tool is talked into running an attacker's code. We sandbox execution and tightly scope what it can run.
Memory & context poisoning
Bad data planted in an agent's memory sticks as trusted fact, quietly corrupting its decisions long after the attack itself.
Insecure inter-agent comms
Messages between agents get forged, spoofed or intercepted, so one agent lies to another. We authenticate and sign agent-to-agent messages.
Cascading failures
One failure snowballs across a multi-agent workflow. Circuit breakers and blast-radius limits stop the chain reaction early.
Rogue agents
An agent drifts off its mandate and starts acting on its own agenda. Behavioral baselines and a kill switch catch it fast.
Human-agent trust exploitation
OWASP's ninth risk is not the machine, it is the person who trusts the agent's output too much and waves it through. So our hand on the wheel is not blind sign-off. It is trained reviewers, calibrated trust and verification at the high-stakes gates, so the human in the loop is a control, not a fresh way in. If a provider promises "a human reviewer" without mentioning this, that is the question to ask them.
Ten failure modes, one question: which can reach your data today? Let us map it →
Not only attackers. Also the model on a bad day.
The OWASP threats above are the malicious end. Real exposure runs across a whole spectrum, from the deliberate and sophisticated to the purely accidental. We design for the entire range, because a hallucination in a contract can cost as much as an injection.
Adversarial and targeted
Nation-state actors, data poisoning, prompt injection and jailbreaks, engineered to bend the agent to someone else's goal.
Supply and data risk
Compromised tools and models, leaked secrets, and training or context data that quietly carries risk into every answer.
The model itself
Bias, overreliance and hallucination. No attacker required, just a confident wrong answer that a tired reviewer waves through.
Most security pages stop at the attackers. The unintentional end, bias, overreliance and hallucination, is where a lot of real damage actually happens, and it needs the same observability and testing, which we build into every rollout with AI Enablement.
Autonomy you can actually trust.
You cannot make an autonomous system perfectly safe. You can make it contained, observable and reversible, so that when something goes wrong, it is small, visible and stoppable. That is the whole game, and it is defense in depth.
Identity & least privilege
- A distinct identity for every agent, never a shared human token
- Short-lived, scoped, per-task permissions
- Secure tool and MCP use, with signed, version-pinned registries
Guardrails & human-in-the-loop
- Input and output guardrails against injection and leakage
- Approval gates on high-impact and irreversible actions
- Isolated, sandboxed execution with hard limits
Observe, red-team & kill
- Full audit trails and a behavioral baseline of normal
- Adversarial red-teaming for injection and tool misuse
- Action budgets, circuit breakers and a one-click kill switch
Agents move faster than any human team. A senior human still owns the guardrails, and the kill switch.
Security across the agent's whole life.
Not a one-off audit. A loop that runs from the first agent you deploy to the hundredth, and keeps running.
Find every agent
Inventory the agents, tools and non-human identities you already run, including the ones nobody formally signed off, and map what each one can reach.
Least privilege by default
Scoped, short-lived identity per agent, secure tool and MCP use, and permissions cut back to exactly what each task needs and nothing more.
Guardrails and gates
Input and output guardrails, approval gates on high-impact and irreversible actions, and sandboxed execution with hard limits.
Attack it first
Adversarial testing for prompt injection, tool misuse and memory poisoning, so you find the failure before an attacker does.
See, stop, prove
A behavioral baseline, full audit trails, a working kill switch, and reporting mapped to the standards, so your posture stays provable over time.
Governed to the standards, not to vibes.
Security by opinion does not survive an audit or an incident. We anchor every engagement to the frameworks the industry is converging on, so your controls are defensible and your posture is measurable.
OWASP, NIST & ISO 42001
The OWASP Top 10 for Agentic Applications for threats, the NIST AI Risk Management Framework for governance, and ISO/IEC 42001 for an AI management system. One shared language, an auditable baseline, and a maturity path from pilot to scale.
EU AI Act & auditability
We map controls to the relevant EU AI Act obligations and keep every agent action logged and reviewable. When a regulator, a board or a customer asks what your agents can do and what they did, you have an answer, not a shrug.
Already running agents in production? Start with an agentic threat assessment →
Six parts. Skip to the one you need.
Everything below is written for security and engineering teams. Each part stands on its own.
No single control is enough. So we layer all of them.
Defense in depth means assuming any one layer can fail, and making sure the next one still holds. Each layer stops a different class of attack, so a breach has to beat every ring, not just the outer one. Here is the architecture that wraps the agents your Process Automation & Agentic AI team builds, from the credential to the kill switch.
Per-agent identity and least privilege
Every agent gets its own scoped, short-lived credentials, never a shared human login.
Guardrails, allowlists and sandboxes
Prompt-injection filters on the way in, a curated allowlist of tools and MCP servers, and sandboxed execution for anything that runs code.
Human gates at the high-stakes moments
Calibrated approval before irreversible or high-value actions, with trained reviewers, not blind sign-off.
Tracing, baselines and anomaly detection
Full traces of what the agent read and did, behavioural baselines, and alerts when it drifts, because non-deterministic systems must be watched, not reviewed once.
Blast-radius limits, circuit breakers and a kill switch
When something gets through, the damage is capped, the chain reaction is broken, and one switch stops the agent, with a breach-mitigation and crisis plan ready.
Running through every layer, continuously: red-team and penetration probes that attack our own defenses before anyone else does.
Security is not a one-off test. It is a pipeline that never stops.
Because models, prompts and tools change constantly, and because outputs are non-deterministic, a single audit ages fast. So we run red teaming as an ongoing loop: generate adversarial tests, evaluate the agent against them, measure, and feed the fixes back in.
1. Generate the test dataset
Collect real prompts and known attack patterns, generate adversarial messages, then apply variations, paraphrases, obfuscations and new injection styles, so the test set keeps up with the threat.
2. Evaluate and mitigate
Run the agent against the dataset, score it automatically, refine the hard cases with a human, track the KPIs over time, and hand the failures to mitigation. Then repeat.
This is the blue-team and red-team loop that a managed AI-security service runs for you, alongside Cloud Services, so the agents your Process Automation & Agentic AI team builds are tested the way an attacker would, on a schedule, not once at launch.
Agents do not keep office hours. Neither does the watch.
Agents act at machine speed, across tools, at three in the morning. Endpoint and log monitoring were built for humans and servers, not for a non-deterministic actor that reasons, calls tools and changes its plan mid-run. Agentic detection and response watches the behaviour of the agent itself, and steps in before a bad run becomes a bad day.
A baseline for every agent
We learn what normal looks like for each agent and each task, so drift, a new tool, an odd data path, an unusual volume, stands out immediately instead of hiding in the logs.
Detect at machine speed
We run continuous monitoring against a mean-time-to-detect service level measured in minutes, not a quarterly review that finds the problem long after the money left.
Respond, not just alert
When an agent goes off pattern, the first response is automatic: throttle it, revoke the tool, or pull the kill switch we build in. A human takes over with the full context already assembled.
One pane, every agent
Traces, alerts and actions from all your agents in one place, tied to the same identities and guardrails we deploy, and connected to your managed Cloud Services SOC.
This is a managed service with a real service level, so the agents your Process Automation & Agentic AI team builds are watched the way an attacker moves: constantly, at speed.
The attacks evolve weekly. Your tests should too.
New jailbreaks, injection techniques, poisoned models and malicious tools appear all the time, and a generic threat feed does not cover them. We maintain an AI-specific intelligence layer that tracks what targets models and agents, and we feed it straight into the red-team loop so your defenses are tested against this week's attacks, not last quarter's.
Track what targets models and agents
New prompt-injection and jailbreak patterns, model and tool poisoning, and data-exfiltration tradecraft, mapped to the OWASP agentic threats so it is actionable, not just news.
Feed the red team automatically
Every new technique becomes an adversarial test in the pipeline that never stops, so coverage grows as the threat grows, without waiting for the next engagement.
Watch your dependencies
We monitor the models, packages and MCP servers you rely on for fresh advisories and compromised releases, and flag the ones that touch your agents.
Brief the humans
A short, plain-language read-out of what changed and what we did about it, so leadership is informed and never blindsided.
Intelligence is only useful when it changes what you test. Ours plugs directly into the continuous red teaming above, and into every rollout we ship with AI Enablement.
When an agent goes wrong, you need to prove exactly what it did.
An incident with an autonomous agent is nothing like a stolen password. The actor is non-human, it took many steps, and it may have touched data and systems along the way. Because we trace everything, we can rebuild the full sequence and act on it. Contain, reconstruct, prove, harden.
Contain first
Isolate the agent, revoke its credentials and tools, and freeze the blast radius with the same kill switch and circuit breakers we build in, so the incident stops spreading while we investigate.
Reconstruct the run
Replay every step from the trace, from the triggering input to the final action, so there is no guesswork about what the agent read, called and changed.
Prove it
A defensible, time-stamped record for auditors, regulators and insurers, aligned to your reporting duties under NIS2 and DORA, produced from evidence rather than reconstruction after the fact.
Learn and harden
Feed the root cause back into the guardrails and the red-team set, so the same path cannot open a second time.
The same tracing that makes agents observable makes them defensible. When something does get through, we can show precisely what happened on the agents your Process Automation & Agentic AI team runs.
An agent is only as safe as the tools it is allowed to call.
Every tool, MCP server and package an agent can reach is part of its attack surface. A poisoned tool description, an over-permissioned connector or a compromised dependency can turn a well-behaved agent into an insider threat. This is the ASI04 supply-chain risk, and we treat the agent's toolset like production software.
Curate and allowlist
The agent gets a reviewed set of tools and MCP servers, each scoped to least privilege, not open access to whatever it can find.
Scan continuously
The tools, their descriptions and their dependencies are scanned for poisoning, permission creep and known vulnerabilities, on a schedule, not once at onboarding.
Sign and verify
We verify the integrity of tools and connectors before the agent uses them, so a swapped or tampered tool is caught before it runs.
Cover web and API surfaces
Where agents touch web and API endpoints, we bring proven application and API security to bear, so the edges the agent reaches are as hard as the core.
This deepens the MCP and tool-security review below into a continuous discipline, because the supply chain does not stand still, and neither can the checks on it.
A focused way to start.
Agentic threat assessment
Discover the agents and tools you already run, map their identities and permissions, and test them against the OWASP agentic threats.
MCP & tool-security review
A hard look at the tools and MCP servers your agents connect to, with poisoning, permission and supply-chain risks surfaced and fixed.
Agent red-team & guardrail design
Adversarial testing for prompt injection and tool misuse, then the guardrails, approval gates and kill switch to close what we find.
Security connects to the rest of the agency.
Autonomy is only worth deploying when it is safe to run, so security sits next to everything agentic.
We build agents, so we know exactly how they break.
Builders and defenders
We design and run autonomous systems for clients, so we secure them from the inside, not from a checklist written by people who never shipped one.
A hand on the wheel
Our whole approach to autonomy is human-in-the-loop with a kill switch. Security is not bolted on, it is how we think about agents in the first place.
Standards, not opinions
OWASP, NIST AI RMF and ISO/IEC 42001, mapped to the EU AI Act, so your posture is auditable and defensible.
Defense in depth
Least privilege, isolation, guardrails and monitoring layered together, so even a successful attack stays small and contained.
From pilot to production
The controls that get agents past the pilot wall and safely into production, where most projects stall for lack of them.
One purpose
The best of digital, marketing, people and technology, aimed at driving your business results.
Certified to ISO 27001 and ISO 9001, with ANI innovation certification and more than 20 security and cloud certifications concentrated in Google and Cisco, part of an agency trusted by 500+ brands since 2018.
Start with a free agentic threat assessment.
Before any engagement, we map the exposure of the agents you already run, at no cost. It is the same assessment we open a paid engagement with.
What the free assessment gives you.
Tell us a little about your setup and a senior security specialist comes back with a genuine read. No cost, no obligation.
- A first look at your agents, tools and identities, and what they can reach
- Your exposure mapped to the OWASP Agentic Top 10
- The highest-risk gaps, and where to start
A specialist reviews every request. This is not an automated scan, and we only use your details to prepare and discuss your assessment.
Tell us a little through our main form and a senior security specialist sets up your free assessment.
Request my free assessment →Goes to our main contact form. A specialist picks it up, not a bot.
The questions we hear most.
Why does AI being non-deterministic matter for security?
What is defense in depth for AI agents?
What is agentic security?
How is agentic security different from our existing security tools?
What is prompt injection, and can you actually stop it?
What is MCP, and why is it a security concern?
Do guardrails make agents useless?
Which frameworks and standards do you follow?
What is ASI09 human-agent trust exploitation?
Do we need this if we only run a few agents?
Can you secure agents we have already deployed?
How fast can you detect an agent going wrong?
If an agent causes an incident, can you prove what it did?
How do you stop the tools and MCP servers an agent uses from becoming the weak link?
Where do the numbers on this page come from?
Give your agents autonomy you can actually trust.
Tell us what your agents can reach and do today. We will show you where the risk is, and put the identity, guardrails and kill switch in place to contain it.
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