Story | 12 February, 2024 Share this on: Empowering Secure AI: A Pioneering Approach to Responsible AI By Manpreet Dash Executive Summary: 1. Responsible AI Framework: Outlines the foundational principles of Responsible AI, emphasizing the crucial role of security in building trustworthy AI systems. 2. AIShield’s Secure AI Approach: Details AIShield's two-step process for AI security, blending pre-deployment evaluation with real-time production assessment, and highlighting AIShield's unique, automated solution to AI security challenges. 3. Benefits of AIShield Platform: Discusses the tangible benefits for organizations using AIShield, from enhancing trust and compliance to significantly reducing vulnerabilities and boosting profitability through AI security. Introduction In an era where digital transformation is not just an option but a necessity, artificial intelligence (AI) stands at the forefront of innovation. However, as AI's capabilities expand, so do the complexities of ensuring these technologies are used responsibly and securely. Responsible AI is not just a buzzword but a fundamental approach to integrating ethics, security, and trustworthiness into AI systems. AIShield emerges as a beacon in this domain, championing the cause of Secure AI as a critical pillar of Responsible AI. This blog delves into the essence of Responsible AI, underscores the paramount importance of security within this framework, and showcases how AIShield is setting new standards in securing AI systems against evolving threats. What is Responsible AI? Responsible AI (sometimes referred to as ethical or trustworthy AI) is a set of principles and normative declarations used to document and regulate how artificial intelligence systems should be developed, deployed, and governed to comply with ethics and laws. In other words, organizations attempting to deploy AI models responsibly first build a framework with pre-defined principles, ethics, and rules to govern AI. Here are the broad six principles lay the foundation for Responsible AI framework across organizations: 1. Security, Reliability and Safety – AI systems are developed in a way that is consistent with organizations’ design ideas, values, and principles to not create harm in the world. Integrating advanced safeguards in AI models is vital to prevent potential harms in human rights areas, including privacy, copyright, and intellectual property. 2. Privacy – With an increased reliance on data to develop and train AI systems, organizations establish requirements to ensure that data is not leaked or disclosed. 3. Fairness – AI systems are designed with quality of service, availability of resources, and a minimization of the potential for stereotyping based on demographics, culture, or other factors. 4. Inclusiveness – AI systems should empower and engage communities around the world, and to do this, organizations partner with under-served minority communities to plan, test, and build AI systems. 5. Transparency – People who create AI systems should be open about how and why they are using AI, and open about the limitations of the system. Additionally, everyone must understand the behavior of AI systems. 6. Accountability – Everyone is accountable for how technology impacts the world. This means organizations seek to consistently enact their principles and taking them into account in everything they do. Responsible AI holds massive economic and societal impact. Responsible AI is overarching approach that has several dimensions such as ‘Fairness’, ‘Interpretability’, ‘Security’, and ‘Privacy’ that guide all of AI product development at the companies who are embracing and adopting Responsible AI. AI Security: A Central Pillar of Responsible AI & Trustworthy AI Framework Security is one of the key and central pillars of Responsible AI. Management of AI risks remains an area of significant improvement with a waning focus on the cybersecurity of AI (McKinsey State of AI 2021), as the adoption of AI continues to rise. 41% of organizations using AI have had security incidents or privacy breaches (Gartner). Attacks on AI systems have severe consequences for society and the innovation ecosystem. Figure 1 - 4 Pillars of AI Trust, Risk, Security Management to Manager Risk | Source: Gartner | https://www.gartner.com/en/articles/what-it-takes-to-make-ai-safe-and-effective Secure AI is a framework for creating a standardized and holistic approach to integrating security and privacy measures into ML-powered applications. It is aligned with the ‘Security’, ‘Safety’, ‘Robustness’ and ‘Privacy’ dimensions of building AI responsibly. Secure AI framework ensures that ML-powered applications are developed in a responsible manner, considering the evolving threat landscape and user expectations. However, AI security and robustness evaluation pose difficult and complex engineering and technological challenges, especially when scaled. Risk management of AI, a key focus supported by NIST’s AI RMF, goes beyond understanding risks – it requires quantification and standardized terminologies for discussions around evaluations of AI system safety. After identifying and quantifying risks, it is crucial for AI builders, testers, auditors to mitigate those risks and provide assurance of safety and technical robustness – ideally through quantifiable metrics. AIShield helps organizations to mitigate AI security risks before and after deployment and make AI systems (including Generative AI) resilient, and secure. This helps to improve the safety and trustworthiness of AI systems, as well as compliance with AI regulations and cybersecurity guidelines. AIShield has actively participated in the development of guidelines, standards, best practices, and benchmarks for assessing the security and safety of AI systems in India and globally. This has been achieved by contributing to working groups of MITRE ATLAS, NASSCOM, DSCI (Data Security Council of India), DoT - Government of India, BIS (Bureau of Indian Standards), ETSI (European Telecommunications Standards Institute), FDA among others. How can practitioners implement AI Security and fairness, explainability, reliability, scalability, and trust? When considering the robustness of AI systems, we (AIShield) adopt a broader perspective of trustworthiness with a central focus on security. Given the significant impact and critical nature of risks associated with AI systems, a security-centered trustworthiness approach is pivotal. This approach leverages system insights (vulnerability, boundary conditions and loopholes) to effectively address key aspects such as fairness, explainability, reliability, scalability, and trust. Our product’s (AIShield Platform) approach is based on the following 2 step process: 1. Pre-Deployment Evaluation: This phase involves a comprehensive assessment of a deployment-ready AI system before its actual deployment. It serves as a point-in-time analysis to evaluate the system against the dimensions mentioned above. During this phase, the model's performance limits, along with its operational boundaries and thresholds, are thoroughly examined. Meeting the success criteria at this stage is crucial for the AI system to be deemed production ready. 2. Real-Time Assessment in Production: Once the AI system has been vetted, it undergoes continuous real-time monitoring during its operational phase. This includes vigilant tracking of both incoming data and any variations in the AI model's performance relative to the established thresholds. This ongoing monitoring facilitates the activation of system-triggered alerts, notifications, and fallback strategies, which are essential for enhancing the system's overall trustworthiness. Figure 2 – Intervention points of AI security in the ML development workflow AIShield’s Approach AIShield addresses the complex engineering challenge of securing AI systems with an innovative, automated, product-based approach – a complete breakthrough in the cybersecurity industry. AIShield AI Security Product ensures the security of all AI models, irrespective of model, deployment, framework, or use case. During our product development, we engaged with over 400+ AI and cybersecurity professionals, gaining deep insights into the core cybersecurity concerns of various stakeholders – from developers and AI owners to security specialists and leadership. The AIShield Platform is an API-based, one-click AI security assessment product, setting it apart in an industry typically dominated by manual models reliant on human knowledge and effort. AIShield’s AI Security platform, a state-of-the-art SaaS offering, is designed to shield AI-driven solutions from adversarial cybersecurity threats comprehensively. The platform initiates with a unique attack framework to pinpoint vulnerabilities in AI models, followed by deploying bespoke defense strategies tailored to each model, effectively countering real-time cybersecurity threats. This end-to-end automated process, supported by patented technology, ranges from in-depth vulnerability assessment to the deploying real-time defenses. Our platform not only identifies AI model security risks and quantifies these risks but also works effectively in both greybox and blackbox settings – ensuring user and builder confidence in model privacy. Furthermore, it provides defense measures and a customized defense model for real-time endpoint security monitoring post deployment. This comprehensive platform also delivers detailed security documentation for each assessed AI model, helping organizations align with global cybersecurity regulations and guidelines such as NIST AI RMF, EU AI Act, and other industry-specific AI security requirements. Key organizations benefit for operationalizing AI security principles with AIShield Platform: Brings trust to AI by securing AI assets against threats.Accelerates time to value with brand and IP protection through automated, consistent defense mechanisms for key IPs and assets.Ensures safety and reliability of AI applications by reducing critical vulnerabilities by 90%.Increases profitability with a fraction of the cost (<2%) of developing an enterprise security solution from scratch, achieving 7x to 15x ROI & 1.5x productivity gain.Enables regulatory and compliance adherence for AI GRC (Governance, Risk & Compliance) and global cybersecurity regulations related to AI. Conclusion In navigating the complex terrain of AI integration, security emerges not just as a feature but as a foundational pillar that holds the promise of trust, reliability, and ethical use of technology. AIShield stands at the vanguard of this movement, embodying the principles of Responsible AI through its innovative Secure AI framework. By prioritizing security at every stage of AI development and deployment, AIShield not only addresses the pressing challenges of today but also lays down the groundwork for a future where AI can be leveraged safely, ethically, and inclusively. As organizations around the globe continue to harness the power of AI, the AIShield Platform offers a beacon of hope, ensuring that the digital frontier is navigated with the utmost integrity and security. Together, we can pave the way for a future where AI empowers humanity, fortified by the unwavering pillars of security and responsibility. Secure Your AI Future with AIShield Facing challenges in securing your AI and ensuring it aligns with Responsible AI principles? AIShield is here to help. Our expert team is ready to provide you with the tools and strategies needed to protect your AI applications and align them with ethical standards. Concerned about AI security and ethics? Looking for expert guidance to navigate these challenges? Fill out our form today for a consultation with AIShield's experts. Let's make your AI initiatives secure, ethical, and successful. Have any feedback or questions? We'd love to hear from you.Contact Us Ready to secure your AI systems? Start your journey today.Book your Demo