Agent security and permissions review
A review of every agent’s access to systems and data, compared with what its job actually needs. Excess permissions are removed and changes are logged.
AI security & governance
Controls that let risk teams approve agents
Controls that let risk teams approve agents
instead of blocking them
We review what your agents can access, test them against adversarial inputs, and set up the governance decisions that let AI move forward with clear limits and audit trails.
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Our clients
KnowlesSterne KesslerRepleniumABBClient fiveClient sixClient sevenClient eight
Security and governance
for AI in production
AI penetration testing
Adversarial testing of AI systems for prompt injection, data leakage, unsafe tool use, and jailbreaks, with findings ranked by impact and fixes verified.
AI governance council setup
An executive forum with a clear charter for approving AI use cases, setting risk thresholds, and reviewing incidents, with templates for each decision.
Autonomy and risk policy
Written rules for what agents may do alone, what needs review, and what stays human, mapped to risk levels your compliance team recognises.
Audit trails and monitoring
Logging of agent actions, inputs, and approvals in a form auditors can read, with alerts for unusual behaviour or access patterns.
Regulatory readiness
Mapping of AI systems against frameworks such as the EU AI Act, NIST AI RMF, and ISO/IEC 42001, with gaps prioritised for action.
Is security holding back your AI roadmap?
Show us what is blocked and we will outline the controls that unblock it.
Book discovery callWhy do AI projects
stall at security review?
Nobody knows what agents can reach
Access was granted during the pilot and never reviewed.
New attack types, old test plans
Standard penetration tests do not cover prompt injection or tool misuse.
Approvals happen case by case
Without agreed risk thresholds, every AI proposal starts the same debate.
Audit trails are incomplete
Logs show that an agent ran, not what it decided or who approved it.
How we put AI controls
in place
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We list every AI system and agent in use, what it can access, and who owns it.
Deliverables: AI system register, access map, ownership list
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Permissions are reviewed against need and AI systems are tested against adversarial techniques.
Deliverables: Permission findings, penetration test report, risk ratings
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We help remediate findings and draft autonomy and risk policies with your security and compliance teams.
Deliverables: Remediation log, autonomy policy, logging standards
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The governance council is chartered and runs its first decisions with tkxel support.
Deliverables: Council charter, decision templates, review calendar
Featured success stories
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Engineers who ship inside your systems,
not isolated AI demos
Talk to an FDE lead
Production from the first sprint
We build in your repositories and environments from day one, so there is no separate rebuild between proof of concept and release.
Senior pods, not rotating benches
Each pod is led by an engineer who has shipped AI systems before, drawn from tkxel’s team of around 150 AI professionals. The same people stay through delivery.
Governance built in, not bolted on
Autonomy matrices, audit trails, and human review are designed with your risk and security teams before release, backed by ISO 27001 controls.
Measured against your metrics
Every engagement starts with agreed business metrics and evaluation sets. We report against them throughout, and the pod exits when your team can run the system.
We've been recognized by the best, year after year
- Inc. 5000
- Top 15 Inspiring Workplaces
- Titan Business Platinum (AI & Automation)
- Financial Times
- Mogul People Leader
- Forbes Coaches Council
- ISO 27001
- ISO 20000
- ISO 9001
- CMMI DEV 3
Get your AI systems ready for security approval
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Frequently asked questions
How is AI penetration testing different from a normal pen test?
Traditional tests target networks and applications. AI testing adds attacks specific to models and agents, such as prompt injection through documents or emails, extracting confidential data through crafted questions, and tricking agents into misusing their tools. We run both kinds where needed.
Do you test third-party AI tools or only custom systems?
Both. For third-party tools we test how they are configured and connected in your environment, which is where most risk sits. For custom systems we also review the code and prompts.
Which frameworks do you align to?
We commonly map to NIST AI RMF, ISO/IEC 42001, the EU AI Act, and OWASP guidance for LLM applications, alongside your existing ISO 27001 or SOC 2 controls.
Can testing make an AI system fully secure?
No. Testing reduces known risks and shows how the system behaves under attack, but new techniques appear regularly. That is why we recommend monitoring and periodic retesting rather than a one-time sign-off.
Who sits on an AI governance council?
Typically a senior sponsor, plus leaders from technology, security, legal or compliance, data, and the business units using AI. The council meets on a fixed schedule and decides on use cases, risk thresholds, and incidents.
How long does a first review take?
An inventory, permission review, and penetration test for a small set of AI systems usually takes four to six weeks. Larger estates are reviewed in waves, starting with the systems that carry the most risk.