1 00:00:00,050 --> 00:00:02,960 Lesson managing third party risks in AI systems. 2 00:00:02,960 --> 00:00:09,050 Managing third party risks in AI systems is a critical aspect of implementing AI governance and risk 3 00:00:09,050 --> 00:00:09,890 management. 4 00:00:10,340 --> 00:00:17,000 The use of third party components, whether they be datasets, algorithms or entire AI systems, introduces 5 00:00:17,030 --> 00:00:22,880 a range of potential risks that must be carefully managed to ensure the integrity, security and ethical 6 00:00:22,880 --> 00:00:24,980 deployment of AI technologies. 7 00:00:25,250 --> 00:00:31,430 The reliance on third party elements necessitates a robust framework to mitigate risks associated with 8 00:00:31,430 --> 00:00:36,230 data privacy, security vulnerabilities, and ethical considerations. 9 00:00:36,470 --> 00:00:42,890 AI systems often require vast amounts of data to function effectively, and third party data sources 10 00:00:42,890 --> 00:00:45,620 are frequently utilized to meet this demand. 11 00:00:45,920 --> 00:00:52,220 However, the use of external data sources introduces the risk of data breaches and privacy violations. 12 00:00:52,220 --> 00:00:59,500 According to a study by Ponemon Institute, the average cost of a data breach in 2020 was $3.86 Million 13 00:00:59,530 --> 00:01:02,980 dollars, with significant portions of these breaches attributed to. 14 00:01:03,010 --> 00:01:04,420 Third party vendors. 15 00:01:04,810 --> 00:01:07,630 Ensuring that third party data providers adhere to. 16 00:01:07,660 --> 00:01:10,720 Stringent data protection standards is paramount. 17 00:01:11,140 --> 00:01:12,550 This can be achieved through. 18 00:01:12,580 --> 00:01:18,550 Thorough vetting processes, regular audits, and the implementation of contractual agreements that 19 00:01:18,550 --> 00:01:24,310 enforce compliance with privacy regulations, such as the General Data Protection Regulation. 20 00:01:25,810 --> 00:01:31,450 Security vulnerabilities are another significant concern when incorporating third party components into 21 00:01:31,480 --> 00:01:32,650 AI systems. 22 00:01:33,250 --> 00:01:38,830 The complexity of AI systems often results in a broad attack surface, and third party components can 23 00:01:38,830 --> 00:01:41,050 introduce additional vulnerabilities. 24 00:01:41,740 --> 00:01:47,410 A notable example is the SolarWinds cyberattack, where attackers compromised a widely used third party 25 00:01:47,440 --> 00:01:52,330 software, leading to significant breaches across multiple organizations. 26 00:01:52,750 --> 00:01:59,190 To mitigate such risks, it is essential to conduct rigorous security assessments of third party Components. 27 00:01:59,190 --> 00:02:04,890 This includes performing regular penetration testing, code reviews, and ensuring that third party 28 00:02:04,920 --> 00:02:07,980 vendors adhere to best practices in cybersecurity. 29 00:02:09,750 --> 00:02:15,000 Ethical considerations are equally important when managing third party risks in AI systems. 30 00:02:15,660 --> 00:02:21,720 The use of third party algorithms and data sets can sometimes result in biased or unfair outcomes. 31 00:02:22,320 --> 00:02:25,110 For instance, a study by Obermaier et al. 32 00:02:25,110 --> 00:02:31,440 Found that an algorithm used in the US health care system exhibited racial bias, leading to disparities 33 00:02:31,440 --> 00:02:33,750 in the allocation of medical resources. 34 00:02:34,200 --> 00:02:39,300 This bias was traced back to the historical data used to train the algorithm, highlighting the importance 35 00:02:39,300 --> 00:02:42,540 of scrutinizing third party data for inherent biases. 36 00:02:42,570 --> 00:02:48,990 Organizations must implement robust mechanisms to evaluate the fairness and ethical implications of 37 00:02:48,990 --> 00:02:50,520 third party components. 38 00:02:50,880 --> 00:02:56,910 This can involve conducting bias audits, fostering transparency in AI processes, and engaging with 39 00:02:56,910 --> 00:03:01,490 diverse stakeholder groups to ensure that AI systems are equitable and just. 40 00:03:03,140 --> 00:03:08,120 Moreover, regulatory compliance is a crucial aspect of managing third party risks. 41 00:03:08,540 --> 00:03:13,910 Various jurisdictions have implemented regulations that govern the use of AI and third party data. 42 00:03:14,240 --> 00:03:20,870 For example, the European Union's GDPR imposes strict requirements on data processing and imposes hefty 43 00:03:20,900 --> 00:03:22,700 fines for non-compliance. 44 00:03:22,940 --> 00:03:29,120 Organizations must ensure that their third party partners comply with relevant regulations and standards. 45 00:03:29,270 --> 00:03:34,970 This can be achieved through comprehensive compliance checks, integrating regulatory requirements into 46 00:03:34,970 --> 00:03:40,340 contractual agreements, and maintaining up to date knowledge of evolving legal frameworks. 47 00:03:42,050 --> 00:03:48,140 The integration of third party AI components also necessitates a focus on intellectual property rights. 48 00:03:48,620 --> 00:03:53,930 Unauthorized use of third party IP can result in legal disputes and financial penalties. 49 00:03:54,590 --> 00:04:00,030 According to a report by the World Intellectual property organization, AI related patent applications 50 00:04:00,030 --> 00:04:04,710 have surged, indicating the growing importance of IP in the AI domain. 51 00:04:05,100 --> 00:04:10,410 Organizations must ensure that they have the appropriate licenses and permissions for third party components, 52 00:04:10,410 --> 00:04:13,080 and that their use complies with IP laws. 53 00:04:13,230 --> 00:04:18,990 This involves conducting thorough IP due diligence, securing necessary licenses, and implementing 54 00:04:18,990 --> 00:04:21,480 robust IP management practices. 55 00:04:23,250 --> 00:04:29,220 In addition to these considerations, it is essential to establish clear governance structures to oversee 56 00:04:29,250 --> 00:04:30,990 third party risk management. 57 00:04:31,020 --> 00:04:36,900 This includes defining roles and responsibilities, implementing risk management policies, and fostering 58 00:04:36,900 --> 00:04:38,550 a culture of accountability. 59 00:04:39,420 --> 00:04:45,240 For example, creating a dedicated risk management team that regularly reviews third party relationships 60 00:04:45,240 --> 00:04:51,390 and assesses associated risks can enhance an organization's ability to manage third party risks effectively. 61 00:04:52,080 --> 00:04:58,280 Furthermore, leveraging technology solutions such as AI powered Risk management platforms can provide 62 00:04:58,280 --> 00:05:03,680 real time insights into third party risks and facilitate proactive risk mitigation. 63 00:05:05,360 --> 00:05:09,800 Training and awareness programs are also vital in managing third party risks. 64 00:05:10,100 --> 00:05:16,100 Employees must be educated on the potential risks associated with third party components, and the importance 65 00:05:16,100 --> 00:05:19,070 of adhering to established risk management protocols. 66 00:05:19,460 --> 00:05:24,890 This can be achieved through regular training sessions, workshops and the dissemination of educational 67 00:05:24,890 --> 00:05:25,820 materials. 68 00:05:26,450 --> 00:05:32,690 By fostering a risk aware culture, organizations can enhance their overall resilience to third party 69 00:05:32,690 --> 00:05:33,500 risks. 70 00:05:35,300 --> 00:05:41,000 Finally, continuous monitoring and improvement are essential components of an effective third party 71 00:05:41,000 --> 00:05:42,620 risk management strategy. 72 00:05:43,220 --> 00:05:49,310 The dynamic nature of AI technologies and the evolving threat landscape necessitate ongoing vigilance. 73 00:05:49,940 --> 00:05:56,320 Organizations should implement continuous monitoring mechanisms to detect and respond to emerging risks 74 00:05:56,320 --> 00:05:57,160 promptly. 75 00:05:57,190 --> 00:06:03,040 This can involve leveraging AI and machine learning tools to identify anomalies, conducting regular 76 00:06:03,040 --> 00:06:09,100 risk assessments, and staying informed about the latest developments in AI governance and risk management. 77 00:06:10,780 --> 00:06:17,050 In conclusion, managing third party risks in AI systems is a multifaceted challenge that requires a 78 00:06:17,050 --> 00:06:23,860 comprehensive and proactive approach by focusing on data privacy, security, ethical considerations, 79 00:06:23,860 --> 00:06:30,070 regulatory compliance, intellectual property governance structures, training, and continuous monitoring, 80 00:06:30,070 --> 00:06:35,020 organizations can effectively mitigate the risks associated with third party components. 81 00:06:35,020 --> 00:06:41,110 This not only ensures the integrity and security of AI systems, but also fosters trust and confidence 82 00:06:41,110 --> 00:06:42,430 among stakeholders. 83 00:06:43,030 --> 00:06:48,820 As AI technologies continue to evolve, the importance of robust third party risk management will only 84 00:06:48,820 --> 00:06:54,310 grow, making it an essential component of AI governance and risk management frameworks.