AI policies that can be applied and demonstrated

What is AI Policies, Security, Risk, and Compliance?

The AI Policy, Security, Risk, and Compliance service translates regulatory obligations, frameworks, and risks specific to AI into a consistent set of operational policies, technical controls, tests, and evidence that the organization can apply, maintain, and demonstrate to internal audits, customers, regulators, or partners. The starting point is the practical interpretation of the relevant frameworks — AI Act (European Artificial Intelligence Regulation), GDPR, ISO/IEC 42001, NIST AI RMF, sectoral guidelines and corporate policies — from a technical and operational perspective, always coordinated with the organization's legal and privacy advisors. Q2BSTUDIO does not replace qualified legal advice or promise automatic certification or guaranteed compliance: we help build the technical controls and evidence that the frameworks require, but the legal interpretation, declaration of compliance and certification are the responsibility of the relevant accredited professionals and bodies. We defined an acceptable use of AI policy that classifies use cases as allowed, restricted (with additional controls) and prohibited, based on processed data, impact on people, regulatory exposure and reputational risk. This policy includes criteria for evaluating new cases without the need to approve each one individually, expediting innovation within known limits. In the area of identity and access, we implement SSO with the corporate provider, design granular roles and permissions (RBAC), set up service accounts with purpose, custody, and rotation, and move secrets, API keys, and tokens to audited secret managers. The goal is to eliminate personal accounts, shared passwords, and static keys as risk vectors. AI guardrails constitute a specific control layer: input and output filters, parameter validation, content policies, tool and action limits, and blocking mechanisms against anomalous behavior. To evaluate its effectiveness, we designed adversarial tests that include prompt injection, data exfiltration, privilege escalation through tools, and instruction bypass. These tests are not a complete pentest of offensive security; are assessments of the robustness of AI controls within the scope of government. If formal pentesting engagement is required, a specific security project is recommended. The evaluation of external vendors and models encompasses contract, data processing, retention, location, sub-processors, security, availability, portability, and service changes. The technical and risk fit of the use case is verified, and the review is repeated in the face of relevant changes, not only at the time of the initial purchase. DLP (data loss prevention) integrates when the platform allows it, limiting what data can be sent to external models, what connectors can be combined, and what information can be included in prompts. Data residency and international transfer are assessed in accordance with GDPR and applicable regulations. Audit evidence is designed from the beginning: inventories, approvals, versions, tests, accesses, logs, revisions and exceptions linked to responsible parties and dates. It defines what evidence is necessary, how much to keep it and how to guarantee its integrity. Automating capture reduces manual work, but requires access controls and traceability. Exception and residual risk management is part of the model: not all risk is eliminated, but each exception is documented with justification, accountability, partial mitigations, review date, and formal acceptance. This service is consulting and implementation, not a training course on AI regulation or security.