Difference between revisions of "Risk, Compliance and Regulation"
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− | |keywords=artificial, intelligence, machine, learning, models | + | |keywords=ChatGPT, artificial, intelligence, machine, learning, GPT-4, GPT-5, NLP, NLG, NLC, NLU, models, data, singularity, moonshot, Sentience, AGI, Emergence, Moonshot, Explainable, TensorFlow, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Hugging Face, OpenAI, Tensorflow, OpenAI, Google, Nvidia, Microsoft, Azure, Amazon, AWS, Meta, LLM, metaverse, assistants, agents, digital twin, IoT, Transhumanism, Immersive Reality, Generative AI, Conversational AI, Perplexity, Bing, You, Bard, Ernie, prompt Engineering LangChain, Video/Image, Vision, End-to-End Speech, Synthesize Speech, Speech Recognition, Stanford, MIT |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools |
− | |description=Helpful resources for your journey with artificial intelligence; videos, articles, techniques, courses, profiles, and tools | + | |
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− | [ | + | [https://www.youtube.com/results?search_query=Risk+Compliance+Regulation+Artificial+Intelligence+deep+machine+learning Youtube search...] |
− | [ | + | [https://www.google.com/search?q=Risk+Compliance+Regulation+Artificial+Intelligence+deep+machine+learning ...Google search] |
+ | |||
+ | * [[Risk, Compliance and Regulation]] ... [[Ethics]] ... [[Privacy]] ... [[Law]] ... [[AI Governance]] ... [[AI Verification and Validation]] | ||
* [[Case Studies]] | * [[Case Studies]] | ||
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** [[Environmental Science]] | ** [[Environmental Science]] | ||
** [[Finance & Accounting]] | ** [[Finance & Accounting]] | ||
*** [[Improper Payments]] | *** [[Improper Payments]] | ||
− | * | + | * [[Cybersecurity]] ... [[Open-Source Intelligence - OSINT |OSINT]] ... [[Cybersecurity Frameworks, Architectures & Roadmaps | Frameworks]] ... [[Cybersecurity References|References]] ... [[Offense - Adversarial Threats/Attacks| Offense]] ... [[National Institute of Standards and Technology (NIST)|NIST]] ... [[U.S. Department of Homeland Security (DHS)| DHS]] ... [[Screening; Passenger, Luggage, & Cargo|Screening]] ... [[Law Enforcement]] ... [[Government Services|Government]] ... [[Defense]] ... [[Joint Capabilities Integration and Development System (JCIDS)#Cybersecurity & Acquisition Lifecycle Integration| Lifecycle Integration]] ... [[Cybersecurity Companies/Products|Products]] ... [[Cybersecurity: Evaluating & Selling|Evaluating]] |
− | * [[AI | + | * [[Cybersecurity Frameworks, Architectures & Roadmaps#Zero Trust|Zero Trust]] |
− | ** [[Data Governance]] | + | * [[Strategy & Tactics]] ... [[Project Management]] ... [[Best Practices]] ... [[Checklists]] ... [[Project Check-in]] ... [[Evaluation]] ... [[Evaluation - Measures|Measures]] |
− | * [[ | + | * [[Policy]] ... [[Policy vs Plan]] ... [[Constitutional AI]] ... [[Trust Region Policy Optimization (TRPO)]] ... [[Policy Gradient (PG)]] ... [[Proximal Policy Optimization (PPO)]] |
− | * [[ | + | * [[Artificial General Intelligence (AGI) to Singularity]] ... [[Inside Out - Curious Optimistic Reasoning| Curious Reasoning]] ... [[Emergence]] ... [[Moonshots]] ... [[Explainable / Interpretable AI|Explainable AI]] ... [[Algorithm Administration#Automated Learning|Automated Learning]] |
− | * [[ | + | * [[Algorithm Administration]] |
− | * [ | + | * [[Data Science]] ... [[Data Governance|Governance]] ... [[Data Preprocessing|Preprocessing]] ... [[Feature Exploration/Learning|Exploration]] ... [[Data Interoperability|Interoperability]] ... [[Algorithm Administration#Master Data Management (MDM)|Master Data Management (MDM)]] ... [[Bias and Variances]] ... [[Benchmarks]] ... [[Datasets]] |
− | + | * [[Analytics]] ... [[Visualization]] ... [[Graphical Tools for Modeling AI Components|Graphical Tools]] ... [[Diagrams for Business Analysis|Diagrams]] & [[Generative AI for Business Analysis|Business Analysis]] ... [[Requirements Management|Requirements]] ... [[Loop]] ... [[Bayes]] ... [[Network Pattern]] | |
+ | * [[Development]] ... [[Notebooks]] ... [[Development#AI Pair Programming Tools|AI Pair Programming]] ... [[Codeless Options, Code Generators, Drag n' Drop|Codeless]] ... [[Hugging Face]] ... [[Algorithm Administration#AIOps/MLOps|AIOps/MLOps]] ... [[Platforms: AI/Machine Learning as a Service (AIaaS/MLaaS)|AIaaS/MLaaS]] | ||
+ | * [[Architectures]] for AI ... [[Generative AI Stack]] ... [[Enterprise Architecture (EA)]] ... [[Enterprise Portfolio Management (EPM)]] ... [[Architecture and Interior Design]] | ||
+ | * [[Assessing Damage]] | ||
+ | * [https://www.york.ac.uk/assuring-autonomy/body-of-knowledge/ Body of Knowledge (BoK) | The Assuring Autonomy International Programme - University of York] | ||
+ | |||
+ | |||
+ | = Risk = | ||
+ | [https://www.youtube.com/results?search_query=Risk+Artificial+Intelligence+deep+machine+learning Youtube search...] | ||
+ | [https://www.google.com/search?q=Risk+Artificial+Intelligence+deep+machine+learning ...Google search] | ||
− | + | * [https://www2.deloitte.com/content/dam/Deloitte/nl/Documents/innovatie/deloitte-nl-innovate-lu-ai-and-risk-management.pdf AI and risk management: Innovating with confidence | Deloitte] | |
− | [ | + | * [https://www.sciencedirect.com/science/article/pii/S0925753518311184 Learning about risk: Machine learning for risk assessment | N. Paltrinieria, L. Comfort, and G. Reniers - Science Direct] |
− | [ | + | * [https://www.plugandplaytechcenter.com/resources/machine-learning-security-3-risks-be-aware/ Machine Learning Security: 3 Risks To Be Aware Of | Rohit Gupta - Plug and Play] |
+ | * [https://www.mckinsey.com/business-functions/risk/our-insights/derisking-machine-learning-and-artificial-intelligence Derisking machine learning and artificial intelligence | B. Babel, K. Buehler, A. Pivonka, B. Richardson, and D. Waldron - McKinsey & Company] | ||
+ | * [https://www.mckinsey.com/business-functions/mckinsey-analytics/our-insights/confronting-the-risks-of-artificial-intelligence# Confronting the risks of artificial intelligence | McKinsey & Company] | ||
+ | * [https://www.securitymagazine.com/articles/91184-artificial-intelligence-use-expected-to-increase-in-risk-and-compliance-efforts?oly_enc_id=2793D1335167C1V Artificial Intelligence Use Expected to Increase in Risk and Compliance Efforts | Security - BNP Media] | ||
+ | * [https://www.forbesindia.com/blog/technology/top-risks-related-to-artificial-intelligence-projects-and-how-to-overcome-them/ Top risks related to Artificial Intelligence projects and how to overcome them | Faisal Husain - Forbes] | ||
+ | * [https://www.tcs.com/content/dam/tcs/pdf/Industries/Banking%20and%20Financial%20Services/State-of-AI-in-Risk-Management.pdf The State of AI in Risk Management: Developing an AI roadmap for risk and compliance in the finance industry | TATA Consultancy Services (TCS)] | ||
+ | * [https://www.brookings.edu/blog/techtank/2023/02/15/nists-ai-risk-management-framework-plants-a-flag-in-the-ai-debate/ NIST’s AI Risk Management Framework plants a flag in the AI debate | Cameron Kerry - Brookings Institution] | ||
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− | + | https://pnptc-media.s3.amazonaws.com/images/machine-learning-security-1.width-800.png | |
<img src="https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Risk/Our%20Insights/Derisking%20machine%20learning%20and%20artificial%20intelligence/SVGZ-Derisking-machine-learning-ex1-Expanded.ashx" width="1000" height="700"> | <img src="https://www.mckinsey.com/~/media/McKinsey/Business%20Functions/Risk/Our%20Insights/Derisking%20machine%20learning%20and%20artificial%20intelligence/SVGZ-Derisking-machine-learning-ex1-Expanded.ashx" width="1000" height="700"> | ||
+ | {|<!-- T --> | ||
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+ | {| class="wikitable" style="width: 550px;" | ||
+ | || | ||
<youtube>hoZJwxzAXpc</youtube> | <youtube>hoZJwxzAXpc</youtube> | ||
+ | <b>Getting Started with Machine Learning in Risk Management | ||
+ | </b><br>This week on Office Hours (https://officehou.rs), our OG hosts Dan Zitting and Kevin Legere are back and are ready to talk machine learning. We get a lot of questions about where to start with machine learning... lots of people in audit, risk, compliance, etc. are talking about it in principle but very few have started on a single practical use. In this episode, Kevin will show you where he actually got started using fancy words like k-means clustering to put machine learning to work in his actual job. Also, his Blue Jays hat in the shot is really gross (no one likes the Blue Jays) but Dan looks like he started combing his hair into a combover. Enjoy! ABOUT SERIES Office Hours is a work of passion to share strategies, technology ideas, and real-world stories that inspire governance, risk management, compliance, and audit professionals to live their biggest impact! Our channel is dedicated to delivering the best stories and strategies in developing GRC programs we've seen across 7,000 organizations in 140 countries around the world. In every episode we'll drill down on a topic that can help you level up - risk management, compliance automation, data analytics, next-generation auditing, robotic process automation, artificial intelligence, etc. | ||
+ | |} | ||
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<youtube>_FFzYkcI8UI</youtube> | <youtube>_FFzYkcI8UI</youtube> | ||
+ | <b>Artificial Intelligence (AI) in risk management - [[IBM]], 4th-IR, [[Microsoft]] & [[Google]] | ||
+ | </b><br>Imperial College Business School, Trestle Group and the IRM gathered experts from [[IBM]], 4th-IR, [[Microsoft]], and [[Google]]. - Bernhard Janischowsky (Trestle) - 00:00:05 - Tarun Ramadorai (Imperial) - 00:01:36 - Nicola Crawford (IRM Chair) - 00:04:25 - Raza Sadiq (IRM SIG Chair) - 00:05:47 - Grace Brasington ([[IBM]]) - 00:06:50 - Frank Luijckx (4th-IR) - 00:17:45 - Martin Moeller ([[Microsoft]]) - 00:26:26 - Matt McNeill ([[Google]]) - 00:33:43 - Panel discussion - 00:45:50 | ||
+ | |} | ||
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<youtube>zqkVf5pjGJs</youtube> | <youtube>zqkVf5pjGJs</youtube> | ||
+ | <b>Machine Learning A revolution in risk management and compliance | ||
+ | </b><br>Training on Machine Learning A revolution in risk management and compliance for FRM part 2 Current Issues including topics like Process of Machine Learning, Machine learning approaches, Application of machine learning approaches within the financial services sector, Application of machine learning in Credit risk and revenue modelling, Application of machine learning in Fraud | ||
+ | Application of machine learning in Surveillance of conduct and market abuse in trading by Vamsidhar Ambatipudi | ||
+ | |} | ||
+ | |<!-- M --> | ||
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<youtube>fdBjaLZeuyQ</youtube> | <youtube>fdBjaLZeuyQ</youtube> | ||
+ | <b>97 How might artificial intelligence affect risk management? | ||
+ | </b><br>There's a lot of talk about artificial intelligence (AI) these days, and how it might change the world in the near future. Can AI help us manage risk better? David's answer is Yes and No! | ||
+ | |} | ||
+ | |}<!-- B --> | ||
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<youtube>_PH5NQqlYQ8</youtube> | <youtube>_PH5NQqlYQ8</youtube> | ||
+ | <b>AI Governance & Risk Management | Kartik Hosanagar | Talks at [[Google]] | ||
+ | </b><br>Join Talks at [[Google]] for a conversation with Kartik Hosanagar, John C. Hower Professor of Technology and Digital Business at Wharton, about his new book A Human’s Guide to Machine Intelligence. The book is the result of years of Professor Hosanagar’s research, and explores the impact of algorithmic decisions on our personal and professional lives, and their unanticipated consequences. Kartik will explore how firms can make use of the tremendous opportunities and potential offered by machine learning and automated decision-making, while also doing their part to ensure algorithms are responsibly deployed. About Kartik: Kartik Hosanagar is the John C. Hower Professor of Technology and Digital Business at the University of Pennsylvania’s Wharton School of Business. Professor Hosanagar’s research focuses on the digital economy, in particular the impact of analytics and algorithms on consumers and society, Internet media, Internet marketing and e-commerce. Kartik has been recognized as one of the world’s top 40 business professors under 40. | ||
+ | |} | ||
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+ | {| class="wikitable" style="width: 550px;" | ||
+ | || | ||
<youtube>GyAWd6wIdok</youtube> | <youtube>GyAWd6wIdok</youtube> | ||
+ | <b>AWS re:Inforce 2019: Leadership Session: Governance, Risk, and Compliance (GRC326-L) | ||
+ | </b><br>Vice President of Security Chad Woolf, Director of Global Security Practice Hart Rossman, and Security Engineer Rima Tanash explain how governance functionality can help ensure consistency in your compliance program. Some specific services covered are [[Amazon]] GuardDuty, AWS Config, AWS CloudTrail, [[Amazon]] CloudWatch, [[Amazon]] Macie, and AWS Security Hub. The speakers also discuss how customers leverage these services in conjunction with each other. Additional attention is paid to the concept of ""elevated assurance,"" including how it may transform the audit industry going forward. Finally, the speakers discuss how AWS secures its own environment, as well as talk about the control frameworks of specific compliance regulations. | ||
+ | |} | ||
+ | |}<!-- B --> | ||
+ | |||
+ | == Using Historical Incident Data to Reduce Risks == | ||
+ | * [https://www.cloudfabrix.com/cfxgenie/ cfxGenie | CloudFabrix] ...Find your IT blind spots, assess problem areas or gain new insights from a sampling of your IT incidents or tickets | ||
+ | |||
+ | https://www.cloudfabrix.com/img/cfxgenie-diagram-1-1.png | ||
+ | {|<!-- T --> | ||
+ | | valign="top" | | ||
+ | {| class="wikitable" style="width: 550px;" | ||
+ | || | ||
+ | <youtube>JXeQyzO6Als</youtube> | ||
+ | <b>CloudFabrix cfxGenie | Free IT Assessment Tool to Find Problem Areas & Accelerate AIOps Adoption | ||
+ | </b><br>CloudFabrix Software Inc Find your IT blind spots and accelerate AIOps adoption with cfxGenie - Map/Zone incidents into quadrants to identify problem areas for prioritization - Cluster incidents based on symptoms and features to understand key problem areas. Get started now with your AIOps transformation journey. Signup for free cfxGenie Cloud Access, visit https://www.cloudfabrix.com/cfxgenie/ | ||
+ | |} | ||
+ | |}<!-- B --> | ||
− | + | = Compliance = | |
− | [ | + | [https://www.youtube.com/results?search_query=Compliance+Artificial+Intelligence+deep+machine+learning Youtube search...] |
− | [ | + | [https://www.google.com/search?q=Compliance+Artificial+Intelligence+deep+machine+learning ...Google search] |
+ | {|<!-- T --> | ||
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<youtube>cHWWhbx2uCo</youtube> | <youtube>cHWWhbx2uCo</youtube> | ||
+ | <b>Transforming Compliance With Artificial Intelligence | ||
+ | </b><br>Learn and grow with more BrightTALK webinars and Talks on FinTech Security right here: https://bit.ly/2q4NciN Join our live webinar to learn which elements of AI are driving value for risk and compliance? (i.e. ML, NLP, [[Robotic Process Automation (RPA)]]). Also learn about AI in practice - how regulated financial institutions are utilizing AI today, why why AI is the only answer to addressing big data compliance challenges, at scale. We will also go over what happened when RegTech met SupTech – how are regulators are applying AI to ease and accelerate compliance processes. | ||
+ | |} | ||
+ | |<!-- M --> | ||
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+ | {| class="wikitable" style="width: 550px;" | ||
+ | || | ||
<youtube>_hJ34Muset4</youtube> | <youtube>_hJ34Muset4</youtube> | ||
+ | <b>Compliance.ai Introduction | ||
+ | </b><br>Compliance.ai provides focused regulatory insight. Saving time, reducing cost & risk, while giving compliance officers much needed peace of mind. | ||
+ | |} | ||
+ | |}<!-- B --> | ||
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<youtube>-UlJv4RJegs</youtube> | <youtube>-UlJv4RJegs</youtube> | ||
+ | <b>AI augmenting compliance processes | ||
+ | </b><br>Jorg Schaper, Global Head of Proposition, KYC & Future Screening Platform, Thomson Reuters, talks about the main challenges organisations are facing with compliance screening, the impact of artificial intelligence and machine learning on KYC and AML compliance processes and how regulation and technology will evolve in the coming years. | ||
+ | |} | ||
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<youtube>-uJ9K1cqShA</youtube> | <youtube>-uJ9K1cqShA</youtube> | ||
+ | <b>Machine learning: The future of compliance? - Sibos 2016 | ||
+ | </b><br>Machine Learning or Artificial Intelligence is gaining greater public and business awareness. From self-driving cars to cancer research, the long-predicted benefits are becoming reality. Machine Learning’s core characteristics – self-learning, intelligent yet consistent decision-making and managing complexity – are all attractive from a compliance [[perspective]]. What new technologies and approaches might support compliance programmes in the (near) future? Is the regulatory environment suitable for their adoption? How can the industry fully leverage the potential benefits? | ||
+ | |} | ||
+ | |}<!-- B --> | ||
− | + | = Regulation = | |
− | [ | + | [https://www.youtube.com/results?search_query=Regulation+Artificial+Intelligence+deep+machine+learning Youtube search...] |
− | [ | + | [https://www.google.com/search?q=Regulation+Artificial+Intelligence+deep+machine+learning ...Google search] |
− | * [ | + | * [https://www.forbes.com/sites/cognitiveworld/2019/03/02/artificial-intelligence-regulation-will-be-impossible/#6028371411ed Artificial Intelligence Regulation May Be Impossible | Michael Spencer - Forbes] |
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<youtube>wxiXxsdSGqw</youtube> | <youtube>wxiXxsdSGqw</youtube> | ||
+ | <b>AuditXPRT: AI Solutions for Regulatory Compliance and Audit | ||
+ | </b><br>A brief introduction to AuditXPRT's iXPRT Platform, a unique Artificial Intelligence solution to automate regulatory compliance and audit. Automate the Mundane. Focus on the Value | ||
+ | |} | ||
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<youtube>Lef1qxhcAj4</youtube> | <youtube>Lef1qxhcAj4</youtube> | ||
+ | <b>Artificial Intelligence - AI - applied to Regulatory Compliance | ||
+ | </b><br>https://BPA.Solutions applies Artificial Intelligence to regulatory compliance. The virtual AI Bot guides end-users to report an incident with their mobile device and log it in the BPA app on [[Microsoft]] Office 365. | ||
+ | |} | ||
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<youtube>Wq7EpdiQRWI</youtube> | <youtube>Wq7EpdiQRWI</youtube> | ||
− | <youtube> | + | <b>Why do we need machine learning to effectively approach regulatory requirements? |
+ | </b><br>avide Boselli, Senior Regulatory Analytics Consultant EMEA, Experian, picks apart the problems of using machine learning to meet regulatory needs. Visit RiskMinds International's official content site for the latest insights into risk management and for more exclusive thought leadership: https://bit.ly/2sav1ZI | ||
+ | |} | ||
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+ | || | ||
+ | <youtube>za5YYUIQIaM</youtube> | ||
+ | <b>Decoding the EU Artificial Intelligence Act | ||
+ | </b><br>The European Parliament recently voted to pass its position on the EU AI Act, the world’s first comprehensive AI regulation. This webinar brings together a distinguished panel of leading AI policy researchers and policymakers from the United States and the European Union who shed light on the key provisions in the draft Act, implications for the tech industry and transatlantic relationship, and what’s next as EU enters trilogue negotiations among the Parliament, Commission, and Council to finalize the EU AI Act. | ||
+ | |||
+ | Speakers: | ||
+ | * Rishi Bommasani: Society Lead, Stanford Center for Research on Foundation Models | ||
+ | * Alex Engler: Fellow in Governance Studies, The Brookings Institution | ||
+ | * Irene Solaiman: Policy Director, Hugging Face | ||
+ | * MEP Dragoș Tudorache: Member; Chair of the Special Committee on Artificial Intelligence in the Digital Age; Co-rapporteur of the EU AI Act, European Parliament | ||
+ | |||
+ | Moderator: Marietje Schaake: HAI International Policy Fellow; International Policy Director, Cyber Policy Center, Stanford University | ||
+ | |} | ||
+ | |}<!-- B --> | ||
− | === [ | + | === [https://www.cognitivescale.com/ CognitiveScale] - [https://www.cognitivescale.com/certifai/ Certifai] === |
− | [ | + | [https://www.youtube.com/results?search_query=CognitiveScale+Certifai+machine+learning YouTube Search] |
− | [ | + | [https://www.google.com/search?q=CognitiveScale+Certifai+machine+learning ...Google search] |
− | * [ | + | * [https://www.cognitivescale.com/cortex/ Cortex Studio] |
− | + | https://2pggys3b7fd63bfvol1w51zt-wpengine.netdna-ssl.com/wp-content/uploads/2019/09/CognitiveScale-Certifai-how-it-works.jpg | |
+ | {|<!-- T --> | ||
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<youtube>lgwaLZX1c-s</youtube> | <youtube>lgwaLZX1c-s</youtube> | ||
+ | <b>Tech Showcase Demo: Cognitive Scale | VB Transform 2019 | ||
+ | </b><br>Tech Showcase Demos: Cognitive Scale, Applied Brain Research, integrate.ai, AI Foundry, D-ID | ||
+ | |} | ||
+ | |}<!-- B --> | ||
− | + | https://www.york.ac.uk/media/assuring-autonomy/images/Web%20image%20-%20SUDA%20model%20(3).png | |
− | * [ | + | * [https://www.americanai.com/artificial-intelligence-governance-firm/ Artificial Intelligence Governance Firm (Civilian Applications) | American Institute] |
− | + | https://www.americanai.com/wp-content/uploads/2016/09/CM.png |
Latest revision as of 17:00, 28 April 2024
Youtube search... ...Google search
- Risk, Compliance and Regulation ... Ethics ... Privacy ... Law ... AI Governance ... AI Verification and Validation
- Case Studies
- Cybersecurity ... OSINT ... Frameworks ... References ... Offense ... NIST ... DHS ... Screening ... Law Enforcement ... Government ... Defense ... Lifecycle Integration ... Products ... Evaluating
- Zero Trust
- Strategy & Tactics ... Project Management ... Best Practices ... Checklists ... Project Check-in ... Evaluation ... Measures
- Policy ... Policy vs Plan ... Constitutional AI ... Trust Region Policy Optimization (TRPO) ... Policy Gradient (PG) ... Proximal Policy Optimization (PPO)
- Artificial General Intelligence (AGI) to Singularity ... Curious Reasoning ... Emergence ... Moonshots ... Explainable AI ... Automated Learning
- Algorithm Administration
- Data Science ... Governance ... Preprocessing ... Exploration ... Interoperability ... Master Data Management (MDM) ... Bias and Variances ... Benchmarks ... Datasets
- Analytics ... Visualization ... Graphical Tools ... Diagrams & Business Analysis ... Requirements ... Loop ... Bayes ... Network Pattern
- Development ... Notebooks ... AI Pair Programming ... Codeless ... Hugging Face ... AIOps/MLOps ... AIaaS/MLaaS
- Architectures for AI ... Generative AI Stack ... Enterprise Architecture (EA) ... Enterprise Portfolio Management (EPM) ... Architecture and Interior Design
- Assessing Damage
- Body of Knowledge (BoK) | The Assuring Autonomy International Programme - University of York
Contents
[hide]Risk
Youtube search... ...Google search
- AI and risk management: Innovating with confidence | Deloitte
- Learning about risk: Machine learning for risk assessment | N. Paltrinieria, L. Comfort, and G. Reniers - Science Direct
- Machine Learning Security: 3 Risks To Be Aware Of | Rohit Gupta - Plug and Play
- Derisking machine learning and artificial intelligence | B. Babel, K. Buehler, A. Pivonka, B. Richardson, and D. Waldron - McKinsey & Company
- Confronting the risks of artificial intelligence | McKinsey & Company
- Artificial Intelligence Use Expected to Increase in Risk and Compliance Efforts | Security - BNP Media
- Top risks related to Artificial Intelligence projects and how to overcome them | Faisal Husain - Forbes
- The State of AI in Risk Management: Developing an AI roadmap for risk and compliance in the finance industry | TATA Consultancy Services (TCS)
- NIST’s AI Risk Management Framework plants a flag in the AI debate | Cameron Kerry - Brookings Institution
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Using Historical Incident Data to Reduce Risks
- cfxGenie | CloudFabrix ...Find your IT blind spots, assess problem areas or gain new insights from a sampling of your IT incidents or tickets
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Compliance
Youtube search... ...Google search
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Regulation
Youtube search... ...Google search
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CognitiveScale - Certifai
YouTube Search ...Google search
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