Security testing that shows where you can be attacked

Each test answers a different question. A vulnerability assessment shows how many known weaknesses you have. A penetration test shows how far an attacker could get. Source code analysis finds flaws before they ship. A social engineering test shows whether your people would let an attacker in. AI security testing covers LLM features that traditional tests miss.

Most organizations combine them over a year. We help you pick the right mix for your risks, your release cycle, and what your auditors or customers expect.

We scan your networks, servers, web applications, and cloud environments for known weaknesses, using authenticated scans where possible for deeper coverage. Our engineers then validate the results, so you are not handed a list of false positives.

Many breaches start with a person, not a server. With your written approval, we run realistic phishing emails, pretext phone calls, and, where agreed, in-person attempts to see whether employees follow your security policies.

Results are reported by team and by attack type, not to embarrass individuals. They show where targeted training will do the most good and give you a baseline to measure improvement against.

We review the source code of your web, mobile, and desktop applications to find security flaws developers commonly overlook, across many programming languages. We combine automated static and dynamic analysis with manual, line-by-line review of the highest-risk code.

Findings are mapped to OWASP and CWE categories and written for developers, with the goal of helping your team build secure coding habits, not just closing tickets.

Penetration testing goes beyond scanning: senior testers chain weaknesses together to show real impact across web apps, APIs, mobile apps, and networks. AI security testing applies the same adversarial approach to LLM features, RAG pipelines, and AI agents. Each has its own page with full details.