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AI is here, and along with it are questions of bias, ethics and how practitioners can upskill—and reskill—to keep pace.
Organizations must understand how to use these new large language models without creating negative societal impacts.
Responsible AI development practices and thoughtful consideration of these challenges are necessary to maximize the benefits of AI while mitigating its potential negative consequences.
With the impending adoption of regulations such as the EU AI Act, organizations must adopt holistic responsible AI programs.
As AI systems proliferate and gain more widespread adoption, safeguards to thwart data poisoning attacks and ensure decisions that reflect human values become more important.
This growing importance of privacy in organizations has led to a shift toward more technical privacy roles, where demand for such roles exceeds talent supply.
Although AI can save time and effort, many users remain unaware of the potential risk to an enterprise’s sensitive data.
There is a need for impactful regulations as well as a general awareness on how these regulations can positively improve the field of AI.
There are fundamental considerations necessary to creating a security and privacy risk management baseline for any AI implementation or deployment.
Zero trust is quite supportive of digital trust; they overlap to a great extent.
Ethical AI occurs when the business, risk and compliance teams not only evaluate the business opportunity that AI brings but carefully consider its impact by sitting in the shoes of users, policymakers and financial market managers.
Given the proliferation of AI/ML solutions, it is important to understand potential areas of concern, especially ones that affect the reputation and trustworthiness of organizations and individuals within the digital ecosystem.
If your organization is exploring ways to derive business value from AI and ML, even just maintaining an awareness of the space and becoming educated about the security, assurance, risk, and governance implications is helpful.
Technology control testing is a comprehensive evaluation of an enterprise’s technological infrastructure, systems and processes.
A solid cybersecurity governance structure (inclusive of cyberrisk management) should entail clear accountability for cybersecurity and unequivocal authority for cyber decision making within an organization.
Organizations across a variety of industries can benefit from a well-planned GRC strategy.
SIF is a growing problem for financial institutions. At the same time, the monetary losses from SIF are difficult to quantify, with estimates ranging between US$20 billion and US$40 billion.
Fog computing and edge computing have great potential to play major roles in the real-time processing of data.
Cybersecurity awareness for employees is nonnegotiable. It helps protect both individuals and organizations by mitigating cyberthreats and protecting sensitive information, which strengthens the overall security posture of an organization.
Managing enterprise networks to meet increasing business requirements in the face of consistent cost reduction pressures can be demanding
