AI Usage and Security

Data Protection and Data Governance

A central question in enterprise AI adoption is what data is sent to language models and how it is processed. Cloud-based LLM services may store inputs for training purposes unless contractually agreed otherwise. Organizations must define clear policies on what types of data may be fed to AI tools — covering source code, customer information, and trade secrets. Softagram helps create practices that enable AI productivity benefits without compromising security.

Prompt Injection and Model Governance

Prompt injection attacks represent a new threat vector where malicious input directs a language model to act against the developer's or user's intent. In an enterprise context, this can mean leaking sensitive information or triggering uncontrolled actions in agent-based systems. A model governance framework defines which models are approved, how their versions are tracked, and what safety boundaries are set for usage. These practices are essential as AI becomes part of business-critical processes.

GDPR and the Regulatory Landscape

The EU's General Data Protection Regulation and the upcoming AI Act set concrete requirements for enterprise AI use. Processing personal data through language models requires a legal basis, and high-risk applications must pass conformity assessments. At Softagram, we closely monitor regulatory developments and help our clients build AI practices that meet both current and future requirements. In practice, this means documented processes, risk assessments, and regular auditing.