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Infoblox
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Image Credit: Infoblox
As adversarial GenAI takes off, threat intel must modernize
- Generative AI, particularly Large Language Models (LLM), is transforming cybersecurity by lowering entry barriers for adversaries to create deceiving content like deepfakes, chatbot automation, and code obfuscation.
- Adversaries are leveraging GenAI tools such as voice cloning for scams, reducing the effort needed to deceive targets with trustworthy audio messages, which can lead to fraud and extortion.
- Using AI-driven chat boxes, cybercriminals can automate interactions, target victims with personalized messages, and exploit trust to gain financial benefits, showcasing the need for predictive threat intelligence.
- Threat actors employ GenAI to obfuscate malicious codes, aiding in evasion tactics, and increasing campaign efficiency while evolving traditional security detection methods.
- By focusing on pre-attack activities and utilizing predictive intelligence based on DNS telemetry, organizations can identify malicious infrastructure early on and stay ahead of GenAI risks.
- Infoblox Threat Intel leverages DNS data combined with threat expertise to intercept actor activities early, achieving a high 'Protection Before Engagement' rate in identifying malicious domains before any interaction occurs.
- Predictive intelligence derived from DNS is highlighted as a strategy to combat GenAI-related threats and prevent organizations from falling victim to evolving cybersecurity challenges.
- Infoblox Threat Intel's success in identifying malicious domains before engagement showcases the effectiveness of predictive intelligence in staying ahead of cyber threats.
- It is crucial for organizations to adopt modern threat research strategies, such as predictive intelligence based on DNS data, to combat the evolving landscape of AI-driven cyber threats.
- For more information on Infoblox Threat Intelligence Research and DNS security workshops, visit the provided links.
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