Adaptive AI Risk & Assurance Framework (AAIRAF)

For Cloud and Embedded Systems

The Problem

Risk

The increasing integration of Artificial Intelligence (AI), Machine Learning (ML), Deep Learning (DL), and emerging paradigms like Generative AI (GenAI) and Agentic AI into critical systems, such as embedded systems, critical infrastructure, and healthcare devices, introduces novel and complex cybersecurity, operational, and ethical risks. Traditional cybersecurity frameworks, while foundational, often lack the specificity to address vulnerabilities unique to AI models, data pipelines, and their operational environments.

The Solution

Framework, Assessment Checklist, Risk Compliance Matrix

The Adaptive AI Risk & Assurance Framework (AAIRAF) is designed to bridge this gap, providing a structured, comprehensive, and adaptable approach to identifying, protecting, detecting, responding to, and recovering from AI-specific threats. AAIRAF enables organizations, particularly with critical systems and within national security contexts, to harness the transformative potential of AI securely and responsibly, ensuring trust, resilience, and compliance throughout the AI lifecycle while safeguarding national security and upholding ethical principles.

CLICK TO VIEW AAIRAF FRAMEWORK

CLICK TO VIEW AAIRAF ASSESSOR CHECKLIST

CLICK TO VIEW AAIRAF TRACEABILITY MATRIX

Guiding Principles

Secure Engineering Always

Human-Centric & Ethical AI

Prioritizing human well-being, safety, fairness, transparency, and accountability in the design and operation of AI systems. This includes mitigating algorithmic bias, ensuring human oversight, and maintaining human control, especially in lethal autonomous weapon systems, critical infrastructure, and healthcare devices.

Risk-Based Approach

Allocating resources and implementing controls commensurate with the likelihood and impact of AI-specific risks, ensuring the most critical assets and potential harms are addressed first.

Adaptability & Continuous Improvement

Recognizing the dynamic nature of AI technology, evolving threat landscapes, and emerging vulnerabilities. The framework promotes iterative assessment, learning, and adaptation.

Transparency & Explainability

Fostering understanding of AI decisions, limitations, and processes to build trust and enable effective governance and incident response, particularly crucial for auditable and accountable embedded systems.

Security-by-Design & Privacy-by-Design

Embedding cybersecurity and privacy considerations into every stage of the AI lifecycle, from initial conceptualization to deployment and beyond.

Collaboration & Information Sharing

Encouraging the exchange of threat intelligence, best practices, and lessons learned across organizations, industry sectors, and research communities, including defense alliances.

Resilience & Mission Assurance

Designing AI systems to withstand and recover from adverse events, including cyberattacks, data corruption, and system failures, to ensure continuity of critical military, infrastructure, and healthcare operations