Security for software and AI systems
Build security into every stage – secure design, review of all code including AI-generated code, hardened cloud and identity, and protection for the AI systems and agents you deploy. We help you find and fix the gaps before attackers do.
AI has widened the attack surface
More code is written by AI, agents now hold access to real systems and attackers use AI too. Security has to cover the code, the cloud, every identity – human or machine – and the AI itself, from design to monitoring.
is the global average cost of a data breach, and one in four malicious breaches was AI-enabled.
- BeforeSecurity testing just before launchNowSecurity in design, every pipeline and production
- BeforeOnly people have accessNowPeople, services and AI agents, each with least privilege
- BeforeCode assumed to be written by peopleNowAI-generated code reviewed like any other code
Scanning, code review or a pen test: what does each find?
Each method catches different problems, so a sound security programme uses several.
| Method | What it finds | When to use it |
|---|---|---|
| Automated scanning | Known vulnerable patterns, outdated libraries and leaked keys | On every commit |
| Manual secure code review | Logic flaws, broken access control and risky AI-generated code | Before major releases and for sensitive features |
| Penetration test | Weaknesses an attacker can exploit in the running system | Before launch and at least once a year |
| Cloud and identity review | Misconfigured storage, excess permissions and weak sign-in | After major infrastructure changes |
| AI red teaming | Prompt injection, data leakage and unsafe agent actions | Before launching AI features and agents |
What this solution covers
This solution combines capabilities from 5 of our services with what is specific to this challenge. Use it whole, or start with one capability.
Threat modelling & secure design
Find how a system could be attacked before it is built – including AI risks such as prompt injection and tool misuse.
Secure code review & security testing
Manual and automated review of code, including AI-generated code, plus dependency scanning and fixing what penetration tests find.
From across our services
Each capability is run by the team that owns it and explained in full on its own service page.
How a typical project runs
- 01
Assess
Threat model, code and cloud review, and the gaps that matter most.
- 02
Fix the basics
Identity, secrets, dependencies and cloud hardening.
- 03
Build it in
Security checks in every pipeline and guardrails for AI features.
- 04
Monitor
Alerts, audit trails and regular reviews.
Selected client work
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Read the articleFrequently asked questions
How do you build security into software projects?
Through threat modelling, secure architecture, code reviews, dependency scanning, least-privilege access, encrypted data and hardened cloud deployments, from the first sprint.
How do you protect AI systems from prompt injection and data leaks?
We limit what each AI feature and agent can access, separate trusted instructions from untrusted content, test with adversarial prompts before release and monitor for unusual behaviour in production.
Is AI-generated code safe to ship?
Only after the same checks as any other code: human review, tests, dependency and secret scanning and, for critical parts, a security review. We apply these to our own AI-assisted work and to apps others have built with AI tools.
Do you work with cybersecurity companies?
Yes. We built a container-based platform on Kubernetes and Azure for Aristiun, a Dutch cybersecurity and cloud security provider.
How do you handle GDPR?
We design data flows with GDPR in mind from day one, for example the GDPR-compliant data enrichment CRM we built for EinMalek in Germany.