Use AI safely and protect your enterprise data
As organizations adopt generative AI, they face new risks such as Prompt Injection, enterprise data leakage and Shadow AI usage. We anchor your AI strategy in a secure framework, test your LLMs against modern attack techniques, and design technical guardrails that prevent your corporate secrets from leaking into training data.
Audit and Compliance: This service delivers a technical security assessment aligned with the EU AI Act, OWASP Top 10 for LLM and the NIST AI Risk Management Framework.
We audit your AI ecosystem grounded in the OWASP Top 10 for LLM standards.
We test your models and RAG/Agent applications from an offensive perspective.
The impact of employees' AI usage habits on data security.
Audit of the technology layers on which AI models run.
A structured, repeatable, outcome-driven process grounded in the OWASP Top 10 for LLM and NIST AI RMF frameworks, protecting your corporate secrets from leaking into training data.
We map the organization's AI use cases and data lineage end to end.
We run targeted Red Teaming against LLM models and applications using OWASP-based scenarios.
We design input/output filtering mechanisms and "Prompt Firewall" solutions tailored to your organization.
We technically validate your compliance with the EU AI Act and local data protection laws (KVKK/GDPR).
At the end of the AI security audit we deliver both a technical protection guide and concrete recommendations for organizational policy.
Attack scenarios and risk assessment tailored to your LLM applications, RAG architectures and MLOps surface.
Get a quoteCertified specialists in prompt injection, training-data poisoning and model extraction assessments.
View certificationsDetails under EU AI Act, NIST AI RMF and ISO/IEC 42001 obligations.
Explore frameworksThe end-to-end AI stack including LLM-based applications, RAG architectures, chatbots, ML APIs and on-prem model deployments.
OWASP LLM Top 10, MITRE ATLAS and NIST AI RMF. Findings are mapped to these frameworks in the report.
Yes. Through model-connected tools (tool calling), RAG sources and automation chains it can turn into data exfiltration and unauthorized actions. It is one of the main focuses of our testing.
Yes. Tests run within the agreed scope under NDA. Data leakage scenarios are validated with controlled evidence, without exposing real data.
Yes. Personal data flows into the model, anonymization and transparency obligations are addressed alongside the technical audit.
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