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Securing Remote OT Operations:

Palo Alto Networks

However, these advancements come with heightened security risks. This expanded attack surface has made OT systems a prime target for cyber threats, underscoring the need for a robust security framework tailored to remote OT environments. This approach minimizes exposure, limiting each users access to essential systems only.

Security 111
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AI coding assistants wave goodbye to junior developers

CIO

Despite mixed early returns , the outcome appears evident: Generative AI coding assistants will remake how software development teams are assembled, with QA and junior developer jobs at risk. AI will handle the rest of the software development roles, including security and compliance reviews, he predicts. “At

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Accelerating insurance policy reviews with generative AI: Verisk’s Mozart companion

AWS Machine Learning - AI

Verisk (Nasdaq: VRSK) is a leading strategic data analytics and technology partner to the global insurance industry, empowering clients to strengthen operating efficiency, improve underwriting and claims outcomes, combat fraud, and make informed decisions about global risks. The following diagram illustrates the solution architecture.

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Unit 42 Predicts the Year of Disruption and Other Top Threats in 2025

Palo Alto Networks

Additionally, the cost of cyber disruption will increase next year as businesses experience downtime due to cyberattacks and scramble to implement defenses fit for the AI-enabled attacker era. This could result in a greater challenge for cybersecurity professionals in defending against and mitigating the effects of such attacks.

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AI-Driven SOC Transformation with Cloudera: Enhancing Security Operations with Agentic AI

Cloudera

Security Operations Centers (SOCs) are the backbone of organizational cybersecurity, responsible for detecting, investigating, and responding to threats in real-time. In this blog, we explore how Agentic AI, powered by Cloudera , enhances SOC effectiveness and ensures secure, efficient operations. What are AI Agents?

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How ML System Design helps us to make better ML products

Xebia

Table of Contents What is Machine Learning System Design? Design Process Clarify requirements Frame problem as an ML task Identify data sources and their availability Model development Serve predictions Observability Iterate on your design What is Machine Learning System Design?

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Ground truth generation and review best practices for evaluating generative AI question-answering with FMEval

AWS Machine Learning - AI

Additionally, see the Generative AI Security Scoping Matrix for guidance on moderating confidential and personally identifiable information (PII) as part of your generative AI solution. A randomly selected percentage of the records in the output file are sampled and flagged for review as part of a human-in-the-loop process.