Integrated approach of AI TriSM & MITRE AI Maturity Assessment Frameworks for enterprises to successfully deploy AI at scale

AI TriSM, which stands for AI Trust, Risk, and Security Management, is a framework that supports AI model governance, trustworthiness, fairness, reliability, efficacy, and data protection. AI TriSM helps organizations identify and mitigate the potential risks associated with using AI models and applications, such as data breaches, bias, ethical issues, and adversarial attacks. As per Gartner, the framework is projected to be a cutting-edge technology in the years ahead, with organizations that embrace AI transparency, trust, and security experiencing a remarkable 50% increase in efficiency in terms of AI Model adoption, business objectives, and user acceptance.

AI models and applications are not innately reliable, trustworthy, fair, and secure. They depend on the quality and integrity of the data, algorithms, and systems that underlie them. Moreover, they operate in complex and dynamic environments, where they may encounter uncertainties, errors, and malicious actors. The challenges in AI adoption include the lack of transparency, interpretability, and explainability of AI models, which can lead to biased and unfair decisions. Other challenges include data privacy, and data security, which can lead to data breaches and cyber-attacks. Therefore, it is essential to monitor and audit AI models and their outcomes, in addition to data privacy to ensure compliance with relevant standards and regulations.

The roadmap of AI TRiSM for enterprises includes three essential frameworks: a) AI Trust, b) AI Risk, and c) AI Security Management. AI TriSM framework enables organizations to proactively identify and mitigate the risks associated with AI models and applications, ensuring that AI systems are compliant, fair, and reliable and protecting data privacy. AI TriSM solutions include:

1. AI auditing and monitoring tools: These tools help measure and evaluate the performance, robustness, and fairness of AI models, and to detect and correct any anomalies, errors, or biases. They also help track and explain the decisions and predictions made by AI models, and to communicate them to relevant stakeholders.

2. AI transparency and explainability techniques: These techniques help make AI models and their inner workings more understandable and interpretable and provide rationales and justifications for their outcomes. They also help increase the trust and confidence of users and consumers in AI models and applications.

3. AI data management and protection methods: These methods help ensure the quality, security, and privacy of the data used to train and test AI models, and to prevent unauthorized access, leakage, or manipulation. They also help comply with data protection laws and regulations, such as GDPR and CCPA.

4. AI security and defense mechanisms: These mechanisms help protect AI models and applications from cyberattacks, such as data poisoning, model stealing, or adversarial examples. They also help enhance the resilience and robustness of AI models and applications against malicious or accidental perturbations.

To address these challenges, businesses must adopt a 11-step process to achieve maturity in the deployment of AI TRiSM. To assess the AI TriSM maturity organizations can adopt the MITRE Artificial Intelligence (AI) Maturity Model (MM), corresponding organizational Assessment Tool (AT), and MITRE ATLAS framework. These tools enable an enterprise to measure progress in AI maturity as it becomes increasingly proficient at incorporating AI technologies and best practices into its work environment. MITRE AI MM comprises six pillars that are recognized as critical to successful AI adoption: Ethical, Equitable, and Responsible Use; Strategy and Resources; Organization; Technology Enablers; Data; and Performance and Application. There are a growing number of vulnerabilities in AI-enabled systems, as the incorporation of AI increases the attack surface of existing systems beyond those of traditional cyber-attacks with different tactics. MITRE ATLAS outlines the progress of attack tactics as Reconnaissance, Resource Development, Initial Access, ML Model Access, Execution, Persistence, Privilege, Escalation, Defense, Evasion, Credential, Access, Discovery, Collection, ML Attack, Staging, Exfiltration, and Impact.

An integrated approach for the enterprise defining the activities across the maturity levels can be illustrated below: -

Maturity Level 1

1. Identify the risks: Businesses must identify the risks associated with AI models and applications, including the risks of bias, unfairness, and data breaches.

Maturity Level 2

2. Develop a risk management plan: Once the risks have been identified, businesses must develop a risk management plan that includes strategies for mitigating these risks by adopting the AI TriSM framework.

3. Defining AI TRiSM framework for the enterprise: Businesses must draw the blueprint and implement the AI TRiSM frameworks, including AI Trust, AI Risk, and AI Security Management, to ensure that AI systems are compliant, fair, and reliable and protect data privacy.

4. Invest in data quality assurance process, data privacy, and data security: Within the specific domain context of the use case, businesses must invest in comprehensive data quality control, anomaly treatment, and continuous monitoring of data quality process within their DataOps, data privacy, and data security to ensure that AI models are accurate, reliable, and secure.

Maturity Level 3

5. Monitor and evaluate: Businesses must monitor and evaluate their AI models and systems using ModelOps to ensure that they are functioning as intended and that the risks associated with these systems are being effectively managed. AI model explainability techniques, such as feature importance, saliency maps, and counterfactuals can help organizations deploy AI TriSM. AI model explainability can also help improve the performance, robustness, and fairness of AI models.

6. AI Security Assessment: Evaluate the implementation of the mitigation steps by applying the security concepts and classes of technologies that can be used to prevent a technique or sub-technique from being successfully executed.

7. Collaborate with stakeholders: Businesses must collaborate with stakeholders, including customers, organization functional teams, partners, and regulators, to ensure that AI systems are compliant, fair, and reliable and protect data privacy.

Maturity Level 4

8. Establish a governance structure: Businesses must establish a governance structure for all AI models across various business use cases that includes policies, procedures, and guidelines for AI model development, deployment, and secured management.

9. Train employees: Businesses must continuously train their employees on the AI TRiSM framework and the risks associated with AI models and applications.

Maturity Level 5

10. Engage in continuous improvement: Businesses must engage in continuous improvement to ensure that their AI systems are up-to-date and that they are effectively managing the risks associated with these systems.

11. Stay informed: Businesses must stay informed about the latest developments in AI TRiSM and the risks associated with AI models and applications.

Given how quickly AI is developing and permeating many industries, the importance of AI TRiSM cannot be overstated. Stakeholders, including governments, organizations, and individuals, must work together to establish standards, laws, and regulations to facilitate the appropriate application of AI. Leaders in the industry should devote resources to research and development to advance the AI TRiSM methodology, tools, and practices. A comprehensive curriculum that equips future professionals with the skills they need to navigate the challenges of an AI-driven world should be offered by academic institutions. Lastly, legislators ought to enact rules that properly balance upholding the public interest with encouraging innovation.

In the end, AI TRiSM is necessary in an AI-driven future to prioritize security, manage risks, and build trust. We can maximize the benefits of artificial intelligence (AI) while reducing its drawbacks by addressing the ethical, legal, and social consequences of these technologies. Society must work to create a future in which artificial intelligence (AI) improves lives and creates a better world for coming generations.