Remove Data privacy Remove Phishing Remove Threat Detection
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AI and Cyber Security: Innovations & Challenges

eSecurity Planet

By identifying deviations from normal patterns, AI can detect potential security incidents before they escalate into serious threats. AI’s impact on cybersecurity is transformative, providing significant advantages such as enhanced threat detection and response. What is the Impact of AI in Cybersecurity?

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NEWS ANALYSIS Q&A: The early going of Generative AI and LLMs impacting cybersecurity

The Last Watchdog

Integrating GenAI and LLMs into cybersecurity frameworks requires overcoming complex challenges, such as ensuring the models can handle the nuances of cyber threats, addressing data privacy concerns, adapting to the dynamic nature of the threat landscape, and dealing with inaccuracies and incomplete data sets that may lead to misleading outputs.

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SHARED INTEL Q&A: My thoughts and opinions about cyber threats — as discussed with OneRep

The Last Watchdog

Byron: On the software side of things, some exciting breakthroughs are about to gain meaningful traction in leveraging machine learning and automation to shape new security platforms and frameworks that are much better suited to helping companies implement cyber hygiene, as well as execute effective, ongoing threat detection and incident response.

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Agentic AI: A New Way to Accelerate Your Security Operations

Digital Shadows

Rapid, Tailored, and Efficient Security Operations Agentic AI takes the outcomes of generative AI, like alert data collection and synthesis, and puts them to work, autonomously managing and mitigating threats in real time. Access to real-time internal and external data to reduce hallucination.

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How often should security audits be?

CyberSecurity Insiders

Enterprises worldwide are trying to defend themselves against attacks such as ransomware, phishing, distributed denial of service and more. Various data privacy and protection laws are emerging to try and protect consumers and their sensitive information.

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Understanding AI risks and how to secure using Zero Trust

CyberSecurity Insiders

This includes using AI to perform more sophisticated phishing attacks, automate the discovery of vulnerabilities, or conduct faster, more effective brute-force attacks. However, concerns have arisen regarding the possible exposure of sensitive customer or proprietary financial data, primarily due to insider threats or misuse.

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Digital Risk Types Demystified: A Strategic Insight into Online Threats

Centraleyes

Data Privacy Risks Data privacy risks involve the unauthorized access, use, or disclosure of sensitive personal information. This category encompasses threats like data breaches and inadequate privacy controls. Examples include malware, phishing attacks, and insider threats.

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