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CMMC policies: A clear case for using Generative AI over people

Free from talent worries: The AI proposition over people


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Challenges in Assessing Written Policies for NIST 800-171 Compliance

Assessing written policies for NIST 800-171 compliance involves multiple challenges. The inherent complexity and diversity of organizational policies, often written in varied formats and terminologies, make it difficult for assessors to ensure consistency and comprehensiveness, potentially obscuring critical compliance-related information. Additionally, the volume of documentation, especially in larger organizations, and the need to manually review and cross-reference these against NIST 800-171 requirements, is not only time-consuming but also prone to human error, which can overwhelm assessors and lead to oversight or misinterpretation of key compliance elements. Furthermore, the dynamic nature of cybersecurity threats and regulatory standards necessitates continuous updates to policies to reflect the latest security practices and regulatory changes, requiring assessors to verify current compliance while ensuring policies are adaptable and forward-looking, a process complicated by the need for a deep understanding of evolving threats and regulatory trends.

PolicyAi and the Math of AI

ComplAi developed AI mathematical models that predict the probability of a sequence of events occurring, based solely on the current state rather than the sequence of events that preceded it. In natural language processing (NLP), this model is crucial as it analyzes and predicts sequences of words or phrases, breaking down text into states and transitions to capture language's statistical properties. By constructing a state transition matrix from a text corpus, where each matrix entry represents the transition probability between words or phrases, the model supports text generation, speech recognition, and machine translation by understanding word relationships. When assessing NIST 800-171 standards, this model's ability to analyze policy documents, model text structure, and identify compliance patterns is invaluable. Trained on compliant documents, it recognizes compliant text characteristics, aiding in the detection of gaps or inconsistencies and enabling a systematic, scalable compliance evaluation process that enhances both accuracy and efficiency.

Leveraging AI to automate the assessment of CMMC policies

PolicyAi leverages AI to automate the assessment of policies for NIST 800-171 compliance by utilizing its predictive capabilities to analyze and interpret large volumes of text, automatically identifying and highlighting sections that correspond to specific compliance requirements. By training the model on a dataset of policies that are already compliant, PolicyAi learns the probabilistic patterns and structures associated with compliance, enabling it to assess new documents with high accuracy. The automation process involves parsing the policy document into individual states or units of text, evaluating the transitions between these states, and comparing them to the learned patterns of compliant documents, which allows PolicyAi to detect adherence to the expected structure and content necessary for compliance. Deviations are flagged for further review by human assessors, streamlining the assessment process and reducing the workload on compliance teams. Moreover, PolicyAi continuously updates and refines its model as it processes more documents, improving its accuracy over time. This dynamic learning capability, crucial in the context of evolving regulatory standards and cybersecurity threats, incorporates feedback from human assessors and new policy documents, ensuring that the assessments remain relevant and accurate. PolicyAi not only enhances the efficiency and consistency of policy assessments but also provides organizations with a robust tool to maintain ongoing compliance with NIST 800-171 standards.