In the realm of cybersecurity, artificial intelligence (AI) and large language models (LLMs) have become powerful tools that can mimic human writing, respond to complex questions, and engage in meaningful conversations to benefit security analysts and operations centers. However, the emergence of data poisoning poses a significant threat to the integrity of these AI models.
Data poisoning involves the malicious manipulation of training data to compromise the performance and integrity of GenAI models. This can result in biased or misleading information being injected into the training data, leading to harmful outcomes such as delivering misleading advice or exposing sensitive information.
LLMs can be hacked during the training phase or the model’s inference time, allowing attackers to exploit vulnerabilities and compromise the AI system. To prevent data poisoning, robust security measures such as data validation techniques, anomaly detection, and precise language models in benchmark tests must be implemented.
Security protections such as maintaining strict control over data augmentation, enforcing continuous integration and delivery practices, and adopting AI-specific defenses like adversarial training are essential for the safe deployment of LLMs in CSPs and enterprises. By prioritizing security measures and best practices, organizations can leverage the full potential of LLMs while safeguarding against cyber risks in the digital future.
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