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GUEST ESSAY: Why internal IT teams are ill-equipped to adequately address cyber risks

The Last Watchdog

Related: The case for augmented reality training Because of this, cybersecurity investments and regulatory oversight are increasing at an astounding rate , especially for those in the financial services industry, bringing an overwhelming feeling to chief compliance officers without dedicated security teams.

Risk 200
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Historic Charges: First Enforcement Action Filed by New York Department of Financial Services Under Cybersecurity Regulation

Data Matters

On July 21, 2020, the New York State Department of Financial Services (NYDFS or the Department) issued a statement of charges and notice of hearing (the Statement) against First American Title Insurance Company (First American) for violations of the Department’s Cybersecurity Requirements for Financial Services Companies, 23 N.Y.C.R.R.

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5 things to know: Driving innovation with AI and hybrid cloud in the year ahead

IBM Big Data Hub

For organizations of all types—and especially those in highly regulated industries such as financial services, government, healthcare and telco—considerations including the rise of generative AI, evolving regulations and data sovereignty laws and ongoing security challenges must be top of mind.

Cloud 67
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At IBM Think, partners are front and center

IBM Big Data Hub

It’s why we gave partners access to the same training and enablement as IBMers last year, launched a new partner program in January, and continue investing in and growing the IBM Ecosystem. If you’ve followed IBM over the past few years, you know how critical the IBM Ecosystem is to our growth strategy.

Cloud 98
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How Jamworks protects confidentiality while integrating AI advantages

IBM Big Data Hub

Jamworks ensures the confidentiality of data when leveraging AI Jamworks AI is a powerful notetaking and productivity tool that records, transcribes, summarizes and generates meaningful insights from meetings, conversations and lectures. This ensures that students can trust the output in front of them.

Cloud 100
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Conversational AI use cases for enterprises

IBM Big Data Hub

Enterprises can use NLU to offer personalized experiences for their users at scale and meet customer needs without human intervention. DL models can improve over time through further training and exposure to more data. Clean data is fundamental for training your AI. AI training is a continuous process.

Analytics 101
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Confidential Containers with Red Hat OpenShift Container Platform and IBM® Secure Execution for Linux

IBM Big Data Hub

Some example use cases to highlight: Confidential AI: leverage trustworthy AI and while ensuring the integrity of the models and confidentiality of data Organizations leveraging AI models often encounter challenges related to the privacy and security of the data used for training and the integrity of the AI models themselves.