Framework for Operational Resilience and Trust (FORT)

We propose the Framework for Operational Resilience and Trust (FORT), an innovative solution addressing critical gaps in AI safety and explainability. AI adoption in critical sectors such as military and healthcare poses significant risks, as exploitation of these systems could lead to devastating outcomes. FORT enhances AI content moderation by employing novel linear layer conductance techniques, increasing analysis efficiency without adding significant computational overhead. This method provides deeper insights into token relationships and patterns influencing AI outputs, empowering developers and end users to precisely moderate AI-generated content and prevent harmful outputs. FORT also incorporates a standardized AI Bill of Materials (AIBOM) to track foundation model information across training and fine-tunes which may fundamentally change model behavior. This enables users to trace vulnerabilities and ensure model provenance, reducing risks from inherited flaws. Designed to align with NIST’s AI Risk Management Framework, AIBOM information will be stored in a rapidly searchable database, streamlining cross-referencing processes. By automating complex analysis and enhancing model explainability, FORT ensures safer and more trustworthy AI systems. This capability is critical for reducing risk in high-stakes deployments, ensuring AI can be used confidently in roles where safety and reliability are paramount.