Security for the whole stack.

An LLM feature adds an attack surface that traditional application security does not cover: prompt injection, leakage of training and context data, insecure handling of model output, and agents with tool access that can be manipulated into acting on an attacker's behalf. Most teams shipping AI have nobody whose job it is to test for these, and their existing scanner cannot tell an AI endpoint from any other API.

Securetom, our scanner, closes the discovery gap. Enter a domain and it runs 10 modules in one pass: traditional checks built on OWASP methodology for DNS, TLS, headers, and exposed services, plus AI-specific modules that detect exposed LLM and ML endpoints, test for prompt injection, and score the result against EU AI Act and NIST AI RMF. No agent to install, no credentials to hand over, and a report in minutes.

Our team takes it from there: AI security audits and LLM red teaming with CVSS-scored findings and a remediation roadmap, penetration tests with reproduction steps, compliance gap analysis mapped to EU AI Act, NIST AI RMF, ISO 42001, HIPAA, SOC 2, GDPR, and PCI DSS, and managed monitoring with SLA-backed response. We hold ISO 27001:2022 certification ourselves, and because we also run cloud infrastructure and ship the applications on it, findings come with a fix, not only a ticket.