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What is Cybersecurity Risk? Definition & Factors to Consider

  Cybersecurity risk has become a leading priority for organizations as they embrace digital transformation and leverage advanced technology solutions to drive business growth and optimize efficiencies. Additionally, many organizations are increasingly reliant on third-party and   fourth-party vendors   or programs.  In this post, we’ll explore what cybersecurity risk is and take a look at some key cybersecurity risk factors that organizations across all industries should keep in mind as they build and refine their   cybersecurity risk management strategy .   What is cybersecurity risk? Cybersecurity risk refers to   potential threats and vulnerabilities   in digital systems. It encompasses the likelihood of a cyberattack compromising data or systems, leading to financial,   reputational , or operational damage. A few examples of cybersecurity risks include   ransomware ,   malware ,   insider threats ,   phishing attacks ,   poor compliance management , and more. Across industries, cy
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The evolution and future of AI

                  The evolution and future of AI The original AI researchers were very interested in games because they were extremely complex. Huge numbers of possible positions and gains were available, yet they're simple in a certain way. They're simple in that the moves are well-defined, the goals are well-defined. So you don't have to solve everything all at once. With chess in particular, in the work on Deep Blue at IBM, what became apparent, what computers could do on our problem like that was bringing a massive amount of compute resource to do deeper searches, to investigate more options of moves in chess than was previously possible. Watson defeating jeopardy. So this was another crossover point, in the development of AI and cognitive computing. That the questions that IBM was able to answer with jeopardy were questions that weren't simply looking up in the database, and finding the answer somewhere. Rather it required information retrieval over lots of differe
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AI Ethics, Governance, and ESG

            AI Ethics, Governance, and ESG Welcome to AI ethics, governance, and ESG. In this document, you will learn about what AI governance is and what it accomplishes, what ESG is and what it accomplishes, and how governance and ESG connect to AI ethics? Governance is the organization's act of governing through its corporate instructions, staff, its processes and systems to direct, evaluate, and monitor, and to take corrective action throughout the AI lifecycle to provide assurance that an AI system is operating as an organization intends it to, and as stakeholders expected to, and as may be required by relevant regulation. The objective of governance is to deliver trustworthy AI by establishing requirements for accountability, responsibility and oversight. Governance provides many benefits, including, for example, trust. When AI activities are aligned with values organizations can build systems that are transparent, fair and trustworthy, boosting client satisfaction and brand
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AI Ethics and Regulations

                 AI Ethics and Regulations Welcome to AI ethics and regulations. In this document, you will learn about what an AI regulation is, how AI regulations connect with AI ethics, and why it's important to understand AI regulations if you work with AI. A regulation is a government rule enforceable by law. The landscape of regulations around AI is evolving rapidly. And it's important to understand key pieces of regulation in order to design, develop, and deploy, and use AI legally and ethically. IBM's position is that we call for precision regulation of artificial intelligence. And we support targeted policies that would increase the responsibilities for companies to develop and operate trustworthy AI. Precision regulation of AI refers to a regulation that aims to be risk-based, context specific, and which allocates responsibility to the party that is closest to the risk, which might shift throughout the AI lifecycle. Specifically, IBM has proposed a precision regul
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Understanding Bias and AI

                 Understanding Bias and AI Welcome to understanding bias in AI. Here you will learn about bias in the context of AI. You will find out how bias can emerge in AI, how bias can impact AI's outcomes, and how to begin mitigating potential bias in AI.  So bias and artificial intelligence is all about unwanted behaviors. Where AI systems that are used in consequential decision making tasks, whether it's in lending, hiring, even in criminal justice, gives systematic disadvantages to certain groups and individuals. So an example would be an AI system that's helping make decisions on who should receive extra preventative healthcare and it gives more of that to white people than black people. Or another example is hiring algorithm where the AI system actually gives more qualified men a chance to interview than qualified women. There's actually many sources of this happening. So when we talk about AI or machine learning systems, they're trained on historical de
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Defining AI Ethics

                           Defining AI Ethics Welcome to Defining AI Ethics. Humans rely on culturally agreed-upon morals and standards of action — or ethics — to guide their decision-making, especially for decisions that impact others. As AI is increasingly used to automate and augment decision-making, it is critical that AI is built with ethics at the core so its outcomes align with human ethics and expectations. AI ethics is a multidisciplinary field that investigates how to maximize AI's beneficial impacts while reducing risks and adverse impacts. It explores issues like data responsibility and privacy, inclusion, moral agency, value alignment, accountability, and technology misuse …to understand how to build and use AI in ways that align with human ethics and expectations.  There are five pillars for AI ethics: explainability, fairness, robustness, transparency, and privacy.  These pillars are focus areas that help us take action to build and use AI ethically.  Explainability 
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