Security & Compliance
Ensuring robust security practices and achieving compliance with regulatory frameworks is fundamental to responsibly utilizing AI and large language model technologies.
The Adversarial Robustness dimension of security explicitly focuses on proactively defending AI systems against intentional manipulation, attacks, and misuse by malicious actors.
Clearly defined methodologies and tools enabling SMBs to:
Proactively identify and document potential adversarial threats, attacks, and exploitation scenarios unique to AI and LLM systems.
Evaluate and map the specific attack surfaces of LLM integrations, API endpoints, model inference capabilities, and training pipelines.
Implement structured threat modeling processes aligned with industry standards (e.g., STRIDE, MITRE ATT&CK) customized specifically for AI-based systems.
Continuously document and review security assumptions, capabilities, and limitations to maintain proactive readiness against evolving adversarial threats.
A comprehensive governance and control sub-framework addressing all forms of model misuse, adversarial manipulation, and logical exploitation, ensuring holistic protection across internal and external threat vectors.
Abuse Scenario Mapping
Align model misuse patterns to the defined attack surfaces in 3.1.1, referencing each to its entry in the AI Threat Risk Register (R1–R21).
Behavioral Baseline Modeling
Establish baseline behaviors for expected model input-output flows, flagging deviations that indicate adversarial behavior or misuse attempts.
Abuse Detection Controls
Implement real-time monitoring and scoring mechanisms (e.g., anomaly detection, pattern clustering) to identify model manipulation or policy evasion attempts.
Preventive Control Systems
Apply adaptive and preventive security systems which actively neutralize.
Shadow and Insider Use Governance
Track unapproved AI usage, unauthorized fine-tuning, or unsanctioned model integrations via centralized logging and access controls.
Feedback and Retraining Loops
Integrate abuse event logs into retraining pipelines to enhance resilience against recurring manipulation tactics.
Comprehensive operational practices that maintain the security, integrity, and reliability of AI models and their supporting infrastructure throughout the entire lifecycle maintain integrity, availability, and confidentiality throughout deployment and evolution.
Model extraction attacks aimed at reverse-engineering proprietary model logic, parameter settings, training datasets, or internal states.
Inversion attacks designed specifically to compromise model confidentiality, uncover sensitive training data, or reconstruct restricted model details.
Implement model deployment safeguards such as controlled access, restricted APIs, query monitoring, adversarial detection software, and removal of sensitive model artifacts.
Explicit standards and protocols for routine, ongoing AI security testing focusing on:
Formally scheduled penetration testing, red teaming, security audits, and robustness evaluations of AI systems and model interfaces.
Continuous integration of adversarial defense benchmarks and robustness tests into DevOps cycles, model retraining workflows, and model updating procedures.
Maintaining updated threat databases, benchmarks, and industry security frameworks to enhance effectiveness of defensive capabilities continuously.
This section provides structured practices ensuring secure AI deployment while rigorously safeguarding privacy, data confidentiality, integrity, and availability within SMB contexts.
Explicit guidelines for:
Classifying AI and training data based on sensitivity, value, regulatory implications, and risk exposure.
Clear documentation on secure storage, transfer, retention, review, and disposal workflows tailored to classified data levels.
Utilizing standards-based encryption, data anonymization, pseudonymization, masking techniques, and access controls informed by data classification schemes.
Clear, explicit alignment to privacy regulatory frameworks including, but not limited to, GDPR, CPRA/CCPA, and HIPAA, through:
Ensuring AI data collection processes adhere strictly to clear legal bases, consent frameworks, and purpose-specific processing.
Documenting protocols and procedures for ongoing privacy impact and risk assessments explicitly targeting AI deployments.
Continuously updating business practices according to evolving regulatory interpretations and international privacy standard developments.
Definitions and methodologies focused on:
Implementing robust role-based access control (RBAC), least privilege practices, and secure authentication specifically designed for AI model deployments and management platforms.
Regular auditing, monitoring, and enforcement of authentication, authorization workflows, and privilege escalation events.
Clear guidelines enforcing periodic access audits, timely revocation, termination, and provisioning of user permissions aligned explicitly to AI system responsibilities.
Clear and actionable incident management strategies for AI systems and infrastructure, emphasizing:
Specific AI incident categories, escalation pathways, and response protocols.
Timely reporting, documentation, containment, recovery, and investigation procedures explicitly articulated and differentiated for AI, security, privacy, and model-related incidents.
Lessons-learned integration into continuous enhancement, mitigation planning, and vulnerability management efforts.
Practices and explicit guidance ensuring SMBs effectively handle:
Structured model version control, timely updates, secure retirement processes, and removal from production environments.
Clearly defined methodologies and approaches enabling effective data unlearning, privacy-driven retraining requirements, and removal of compromised or unauthorized data from AI datasets.
Governance processes for documenting update and data-retirement decisions, auditing commitments, and maintaining robust compliance standards.
Practical guidance enabling SMBs explicitly to structure, document, evidence, and manage AI solutions in strict accordance with local, national, and international regulatory obligations.
Detailed overview and clear articulation of regulatory applicability concerning:
Current AI regulatory requirements, standards bodies, international frameworks, industry vertical-specific standards, and emerging guidelines.
Applicability mapping clearly linking specific AI use cases, model functionalities, datasets, and implementation contexts directly to particular regulatory scopes.
Explicitly documented alignment clearly articulated for measurable implementation across:
NIST AI Risk Management Framework: Risk identification, mapping, evaluation, and mitigation strategy plans.
EU AI Act obligations: High-risk AI implications, transparency obligations, and explicit required documentation.
GDPR compliance: Data protection impact assessments (DPIAs), data subject rights, processing legality, privacy by design requirements explicitly correlated to AI workflows.
HIPAA requirements: Healthcare-specific AI context addressing Protected Health Information (PHI) management, security, risk assessment, incident notification, and enforcement measures.
Structured practical methodologies supporting SMBs in demonstrating compliance through:
Regularly scheduled internal and external audits, explicit audit criteria, and formal compliance documentation procedures.
Clear governance responsibilities, documentation retention procedures, and explicit compliance evidence management practices.
Simple, standardized processes enabling consistent, traceable, and auditable compliance practices across AI and LLM deployment cycles.
Clear, practical reporting and disclosure requirements applicable explicitly to AI deployment environments, including:
Explicit documentation standards and timeliness criteria for required reports and disclosures mandated under GDPR, HIPAA, the EU AI Act, and other applicable regulations.
Defined criteria triggering mandatory incident disclosures and structured templates for timely communication to supervisory authorities, customers, affected individuals, and regulatory bodies.
Explicit incident reporting escalation paths, responsibilities for reporting, and structured procedures enabling timely fulfillment of legal disclosure obligations.
Domain Progress
0 total assessments across 3 disciplines
Disciplines