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Episode 256: Recursive Pollution? Data Feudalism? Gary McGraw On LLM Insecurity

The Security Ledger

Paul speaks with Gary McGraw of the Berryville Institute of Machine Learning (BIML), about the risks facing large language model machine learning and artificial intelligence, and how organizations looking to leverage artificial intelligence and LLMs can insulate themselves from those risks.

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The business case for security strategy and architecture

Notice Bored

c omplementing and supporting various other business strategies and architectures such as cloud first, artificial intelligence, IIoT, big data, new products, new markets.); Strategy is perhaps the most difficult and risky part of information risk and security, as it is for other aspects of enterprise management.

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Hyperautomation and Cybersecurity – A Platform Approach to Telemetry Architectures

McAfee

Hyperautomation is a process where artificial intelligence (AI), machine learning (ML), event-driven software, and other tools are used to automate as many business and IT processes as possible. Some cyber defenders need more than traditional cyber threat intelligence telemetry to make critical operational impact decisions.

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News alert: ACM TechBrief lays out risks, policy implications of generative AI technologies

The Last Watchdog

27, 2023 – ACM, the Association for Computing Machinery has released “ TechBrief: Generative Artificial Intelligence.” To mitigate these risks, the authors contend that AI law and policy should incorporate end-to-end governance approaches that address risks comprehensively and “by design.” New York, NY, Sept.

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Ask These 5 AI Cybersecurity Questions for a More Secure Approach to Adversarial Machine Learning

NetSpi Executives

Artificial Intelligence (AI) and Machine Learning (ML) present limitless possibilities for enhancing business processes, but they also expand the potential for malicious actors to exploit security risks. How transparent is the model architecture? Will the architecture details be publicly available or proprietary?

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NIST Report Highlights Rising Tide of Threats Facing AI Systems

SecureWorld News

Artificial intelligence (AI) promises to transform major sectors like healthcare, transportation, finance, and government over the coming years. As adoption accelerates, so too do emerging cybersecurity risks. Continuous risk assessment and governance throughout the AI system lifecycle remains essential.

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The Evolving Landscape of Cybersecurity: Trends and Challenges

CyberSecurity Insiders

Artificial Intelligence (AI) and Machine Learning (ML) in Cybersecurity: AI and ML are transforming the way we approach cybersecurity. They provide advanced capabilities to detect and respond to threats by analyzing vast amounts of data, identifying patterns, and predicting potential risks.