Journey towards Trustworthy AI  -  Navigating the Risks of AI Adoption for your enterprise with action plan

AI is rapidly transforming our world and everyday life, with its adoption increasing across various industries such as healthcare, BFSI, automotive, telecommunication, and manufacturing. However, with this increased adoption comes a range of risks that organizations need to be aware of and take steps to mitigate.

One of the main risks associated with AI is bias in algorithms and data, which can lead to unfair and discriminatory outcomes. This is particularly concerning in sectors such as healthcare, where biased algorithms can have serious consequences for patient care (Kleinberg et al., 2018).

Safety risks are another area of concern, as AI systems have the potential to cause harm to humans or the environment if they malfunction or are misused. This is particularly relevant in industries such as automotive or healthcare, where AI algorithms make decisions on the road or for patient wellbeing and safety.

AI also brings with it economic risks. There are also ethical risks to consider, such as the potential for AI to be used for malicious purposes or to infringe on people's privacy and rights. On a global scale, there are strategic risks to consider, such as the potential for AI to be used as a tool for geopolitical advantage by nation states (Council on Foreign Relations, 2021).

Security risks are also a concern, with the potential for hackers to exploit vulnerabilities in AI systems. This is especially true in industries such as BFSI, where AI systems are used to make financial decisions and process sensitive data. One major security risk is adversarial machine learning, which refers to the use of maliciously crafted inputs to deceive or mislead AI systems.

The risks are there but the benefits of AI are even bigger. Therefore as a organization, you can not afford not to use AI and therefore the preferred way is to understand the risk and mitigate it at organization level.

So what can organizations do to mitigate these risks? Some steps that can be taken include:

• Conducting regular security assessments to identify and address potential vulnerabilities in AI systems
• Implementing robust data protection and privacy measures
• Developing clear policies and procedures for the use of AI, including guidelines for reporting and addressing security incidents
• Training employees on security best practices and ensuring they are aware of the potential security risks associated with AI
• Working with third-party vendors and partners to ensure that their AI systems are secure and compliant with relevant regulations and standards
• Investing in robust security infrastructure such as firewalls and intrusion detection systems
• Regularly monitoring AI systems and implementing appropriate measures to address any security issues that may arise

To learn more about how to mitigate the security risks associated with AI, check out our in-depth guide which provides a comprehensive action plan, details on the deliverables, and much more.

To learn more about how to mitigate the security risks associated with AI, check out our in-depth guide which provides a comprehensive action plan, details on the deliverables, and much more. Additionally, we will send you our comprehensive AI Security Buyer's Guide if you're interested.

• Council on Foreign Relations (2021). The Global Consequences of Artificial Intelligence. Retrieved from https://www.cfr.org/in-brief/global-consequences-artificial-intelligence
• DARPA (2018). DARPA Grand Challenge: A Look Back. Retrieved from https://www.darpa.mil/news-events/2018-06-06
• European Union (2019). Artificial intelligence: a European approach to excellence and trust. Retrieved from https://ec.europa.eu/digital-single-market/en/artificial-intelligence-european-approach-excellence-and-trust
• Kleinberg, J., Ludwig, J., Mullainathan, S. (2018). The Economics of Artificial Intelligence: An Agenda. Retrieved from https://www.nber.org/chapters/c14207
• KPMG (2019). Artificial intelligence: A new era of risk? Retrieved from https://assets.kpmg/content/dam/kpmg/xx/pdf/2019/06/artificial-intelligence-risk-report.pdf

Other Sources:

• "Adversarial Machine Learning" by Ian Goodfellow, Jonatha Shlens, and Christian Szegedy (Foundations and Trends in Machine Learning, 2014)
• "Adversarial Examples in the Physical World" by Nicholas Carlini and David Wagner (ACM Conference on Computer and Communications Security, 2016)
• "Adversarial Machine Learning" by Nicholas Carlini, Pratyush Maini, and David Wagner (ACM Conference on Computer and Communications Security, 2017)
• "Adversarial Machine Learning: A Survey" by Kailash Ahirwar and Deependra Kumar Kushwaha (International Journal of Computer Applications, 2018)
• "Adversarial Machine Learning in Healthcare: Threats and Countermeasures" by Chaitanya Mitash and Adwait Nadkarni (Journal of Healthcare Engineering, 2018)
• "Adversarial Machine Learning in Finance: A Survey" by Monique Laurent and Daniel K. Molzahn (arXiv preprint, 2019)
• "Adversarial Machine Learning for Cybersecurity: A Survey" by Kailash Ahirwar, Deependra Kumar Kushwaha, and Pradeep Tomar (IEEE Access, 2019)
• "Adversarial Machine Learning in Natural Language Processing: A Survey" by Kailash Ahirwar, Deependra Kumar Kushwaha, and Pradeep Tomar (ACM Computing Surveys, 2020)
• "Adversarial Machine Learning in Computer Vision: A Survey" by Kailash Ahirwar, Deependra Kumar Kushwaha, and Pradeep Tomar (IEEE Access, 2020)
• "Adversarial Machine Learning: Attacks and Defenses" by Kailash Ahirwar, Deependra Kumar Kushwaha, and Pradeep Tomar (ACM Computing Surveys, 2021)
• "The Risks and Rewards of AI" by Sarah K. D. Robins and E. Glen Weyl (Journal of Economic Literature, 2018)
• "AI Ethics: A Primer" by the Center for Data Innovation (2019)
• "The Ethics of Artificial Intelligence" by John Danaher (Philosophy Compass, 2018)
• "AI Bias: A Practitioner's Guide" by Joy Buolamwini and Timnit Gebru (AI Now Institute, 2018)
• "A Framework for Responsible AI" by the World Economic Forum (2019)
• "The Governance of AI: Ensuring Alignment with Human Values" by Karthik Dinakar and John C. Havens (MIT Sloan Management Review, 2018)
• "AI, Ethics, and Society" by the Future of Life Institute (2018)
• "The Future of Employment: How susceptible are jobs to computerisation?" by Carl Frey and Michael Osborne (Oxford Martin School, 2013)
• "The Societal Implications of AI" by the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems (2017)
• "The Ethics of Artificial Intelligence" by the Markkula Center for Applied Ethics at Santa