Automated AI Assessment

What is an automated AI assessment?

Automated AI assessment tests AI models and systems for fairness, security and compliance against published frameworks, and repeats the test as the models change. DigitalXForce runs automated AI assessment in the AI TRiSCM and AI Risk Governance module. AI TRiSCM and AI Risk Governance is the DigitalXForce module for AI asset discovery, assessment and mapping to NIST AI RMF, ISO/IEC 42001, the EU AI Act, the OWASP LLM Top 10 and MITRE ATLAS. The enterprise AI governance page follows one AI system through its lifecycle, from discovery to evidence.

Which AI systems does DigitalXForce assess?

DigitalXForce assesses models, copilots, agents and RAG stores, along with internal LLMs, prompt libraries and third-party SaaS with embedded AI. The AI TRiSCM module finds these systems across cloud services, code repositories, CI/CD pipelines, containers, model endpoints and data pipelines. Shadow AI appears in the DigitalXForce inventory the same way as sanctioned AI, so it is assessed as well.

What does an automated AI assessment test for?

DigitalXForce assesses each AI system for adversarial risk, such as prompt injection and model extraction. The assessment also checks for data leakage, meaning sensitive data entering or leaving the model. Model integrity covers bias and fairness, drift and hallucination, and the code and supply chain risk around the model.

How often does DigitalXForce repeat an AI assessment?

DigitalXForce assesses each AI system on a schedule and again when the system changes. If a vendor swaps the base model behind an approved product, the assessment runs again when the change appears instead of waiting for the next scheduled date. Refresh triggers keep the AI inventory current, so a new system enters the inventory without anyone having to report it.

Which frameworks does an automated AI assessment map to?

DigitalXForce maps each AI system’s controls once to NIST AI RMF, ISO/IEC 42001, the EU AI Act, the OWASP LLM Top 10 and MITRE ATLAS. The organization’s own AI policy is mapped as well, written as controls with tests. Certification stays with the accredited auditor or certification body, and DigitalXForce hands that auditor the mapped controls and the evidence. The DigitalXForce frameworks page shows the most requested frameworks.

What happens when an AI system fails an assessment?

A failed control around an AI system raises a risk in X-ROC, with its evidence attached and an owner assigned. X-ROC, the XForce Risk Operations Center, is the operations layer of the DigitalXForce TRiSCM platform, where control failures, risk changes and vendor events are alerted, triaged, escalated and reported. AI risks go into the same enterprise risk register as every other risk, where each one gets inherent and residual scores and a treatment. The finding closes only when a retest passes.

How does automated AI assessment relate to Continuous Control Assurance?

The controls around each AI system go through the same validation as every other control in DigitalXForce, as part of Continuous Control Assurance. Continuous Control Assurance (CCA) uses evidence, monitoring and validation to determine whether controls continue to operate as expected. Continuous Control Monitoring (CCM) monitors conditions, evidence and signals associated with controls, and it works as a capability within CCA. In the AI TRiSCM module, CCM keeps the evidence current as the models change.

What reports does an automated AI assessment produce?

XForce GPT writes an executive narrative on the organization’s AI, covering the systems in use, their risk, what changed and what is outstanding. XForce GPT is the DigitalXForce generative AI engine that writes: it produces the plain-language risk narratives and board-ready reports, generates policies, standards and plans, and runs the embedded assistant. Every test result is kept with its timestamp and source, so an auditor can read the evidence behind the report. An analyst reads each XForce GPT draft before the organization acts on it.