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58 rapid expansion of AI, treating SOC 2 or ISO 27001 as the final word on security compliance is no longer acceptable. SEVEN STRATEGIC PILLARS FOR AN AI COMPLIANCE STACK Legal organizations must keep pace with the novel compliance challenges that AI presents, and these seven pillars provide a practical road map for future- proofing compliance. [1] Data Privacy While AI models are valuable, their training data is arguably more so. All your organization's data must still comply with the GDPR, CCPA, HIPAA, or any other relevant data compliance framework, but AI introduces new complexities. Encryption of data in transit and at rest is essential, but proof that personal data is not used to train models without explicit consent is also mandatory. Ensure that training data minimization and pseudonymization practices are implemented where feasible; strictly honor opt-outs from training datasets; and maintain auditable records that show all data lineage and consent. [2] Explainability Clients, regulators, and insurers will not accept a black- box AI model. In highly regulated industries, faith is not a justifiable response to queries about AI decision- making processes; everything must be explainable. Ensure that model cards documenting each model's purpose, training data, limitations, and interpretation guidance are published to enhance trust. Additionally, building explainability features (like ChatGPT's "see sources" feature) into platforms will help users understand why they received a specific output. [3] Bias Audits An AI model with intrinsic biases will produce inequitable outcomes, cause reputational damage, and expose the organization to legal risks. Beyond ethics, governance, as seen in the EU AI Act, dictates that certain biases can be deemed unlawful. The legal industry is built on impartiality and justice; therefore, AI models must also maintain these principles. Fairness audits across demographic groups must be conducted to ensure that disparate impact ratios do not indicate bias. Doing this regularly will allow models to be retrained or reweighted when biases are identified, ensuring their integrity. [4] Risk Management Redefined The scope of SOC 2's risk management is the implementation of controls to ensure that data is secure and available. It does not, however, question whether AI can misclassify or produce illicit content, so the AI risk register must adapt accordingly. Organizations must conform to specific AI risk management frameworks, such as NIST AI RMF or ISO 42001, to new AI-specific risks. As in traditional incident response plans, build escalation paths when AI behavior becomes unexpected or harmful, and establish mitigation strategies. Risk management can no longer be limited to conventional IT risks; the scope must broaden to include the secondary and tertiary risks posed by AI. [5] Rethinking Cybersecurity in the AI Age Law firms and legal tech companies traditionally have very robust perimeter and network security. However, AI introduces novel attack vectors that must be brought under the umbrella of cybersecurity. Attacks such as model inversion, data poisoning, and prompt injection all carry significant cybersecurity risks that did not exist before the advent of AI. Pentesting must broaden to include your models, not just networks, and all AI inference endpoints. AI-specific servers also require real-time monitoring through MXDR and a SIEM. As Risk management can no longer be the scope must broaden to include posed

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