A thorough audit reviews application code and logic, authentication and access control, configuration and infrastructure, and third-party dependencies against standards like the OWASP Top 10 - delivering severity-ranked findings with specific remediation steps for each.

Quarterly is a sensible baseline for business-critical applications, with additional assessments after major releases or infrastructure changes. High-risk or compliance-bound environments often move to monthly scanning with expert review. A single annual scan leaves you blind for eleven months.

AI governance is the policy, control, and oversight framework that keeps AI systems safe, compliant, and accountable. It matters now because regulation has arrived - the EU AI Act's obligations are phasing in - and because enterprise buyers increasingly require evidence of AI risk management before signing.

The leading AI-specific risks include prompt injection (malicious instructions smuggled into inputs), sensitive-data leakage through model outputs, over-permissioned agents taking unintended actions, and supply-chain risks in models and their dependencies. Each requires testing that traditional application security doesn't cover.

Yes - from gap assessment through control implementation and evidence preparation to audit support. We design one control set that maps across frameworks, so adding a second certification later is incremental rather than a second program.