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Architect defense-in-depth security for generative AI applications using the OWASP Top 10 for LLMs

AWS Machine Learning - AI

Many customers are looking for guidance on how to manage security, privacy, and compliance as they develop generative AI applications. This post provides three guided steps to architect risk management strategies while developing generative AI applications using LLMs.

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Cybersecurity Snapshot: Cyber Agencies Offer Secure AI Tips, while Stanford Issues In-Depth AI Trends Analysis, Including of AI Security

Tenable

Check out recommendations for securing AI systems from the Five Eyes cybersecurity agencies. Plus, Stanford University offers a comprehensive review of AI trends. Meanwhile, a new open-source tool aims to simplify SBOM usage. funding, technical expertise), and the infrastructure used (i.e., And much more!

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Cybersecurity Snapshot: New Guide Explains How To Assess If Software Is Secure by Design, While NIST Publishes GenAI Risk Framework

Tenable

The 40-page document seeks “to assist procuring organizations to make informed, risk-based decisions” about digital products and services, and is aimed at executives, cybersecurity teams, product developers, risk advisers, procurement specialists and others. “It What does it take?

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Inference Llama 2 models with real-time response streaming using Amazon SageMaker

AWS Machine Learning - AI

With the rapid adoption of generative AI applications, there is a need for these applications to respond in time to reduce the perceived latency with higher throughput. Foundation models (FMs) are often pre-trained on vast corpora of data with parameters ranging in scale of millions to billions and beyond.

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The Power of AI Assistants and Advanced Threat Detection

Palo Alto Networks

We recently interviewed Mike Spisak, technical managing director with the Proactive Services Creation Team at Unit 42. He discussed his predictions around AI in cybersecurity, and the importance of fostering a cyber-aware culture. Then we can progress to applying AI-specific measures for emerging threats.

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From Hype to Hope: Key Lessons on AI in Security, Innersource, and the Evolving Threat Landscape

Coveros

However, social engineering is a common tactic, so it is advisable to continuously improve security awareness and education in an effort to decrease the effectiveness of social engineering attacks. Only 11% of open source projects are actively maintained. 300+ AI-powered GitHub Actions in the marketplace.

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The Dark Side of AI in Cybersecurity — AI-Generated Malware

Palo Alto Networks

By providing the AI with open-source materials, such as articles analyzing malware campaigns, the researchers were able to generate malware that closely resembled known threats, like the Bumblebee web shell. if you've tried asking generative AI to write a letter like Jane Austen would, the results are scary.

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