Secure SSDLC in the age of generative AI
Implementing AI assistants requires shifting from blind trust in tools to architectural data control and mandatory code ...
Artificial intelligence (AI) is a class of technologies using machine learning, natural language processing, computer vision and generative models to automate analytical and operational tasks.
Implementing AI assistants requires shifting from blind trust in tools to architectural data control and mandatory code ...
How to minimize 'Excessive Agency' risk when implementing AI? Using BPMN orchestration and RLS/RBAC mechanisms to protec...
Integrating AI into industrial systems requires moving away from the "black box" model. We examine a managed agent archi...
Transitioning from pilot AI experiments to industrial Intelligent Document Processing requires architectural oversight a...
For secure AI agent deployment, simple LLM integration is insufficient. A domain data model is the foundation for contro...
How to grant AI agents autonomy without losing data control? We examine architectural barriers, OWASP and NIST standards...
Transitioning from a model-first to a data-centric approach: how data structuring and FinOps discipline help enterprises...
Protecting corporate BPM processes from excessive AI agent autonomy (OWASP LLM08:2025) using strict BPMN 2.0 orchestrati...
Data Contracts as a foundation for protecting corporate AI systems from data manipulation and integration errors. Ensuri...
Implementing Intelligent Document Processing requires transparent logging. Learn how to configure audit trails to mainta...
Transitioning to autonomous AI agents requires robust infrastructure governance, including strict FinOps limits and arch...
How to build data infrastructure for AI without expensive data lakes and endless ETL pipelines using the Data-as-Model a...