As data breaches escalate, companies are increasingly opting for local language models (LLMs) to enhance security. IBM reported over 22 billion records were stolen in 2021, with breach costs exceeding $4.5 million in 2023. Local models maintain data within an organization’s systems, significantly reducing risks associated with third-party vendors, which account for nearly a third of attacks. Businesses benefit from customized data control policies and mechanisms, such as automated anonymization and access restrictions, ensuring compliance with governance standards.
Incorporating AI into existing security frameworks bolsters privacy, with techniques like on-device training, federated learning, and synthetic datasets enhancing confidentiality. However, users must remain vigilant against vulnerabilities, including training data poisoning and sensitive information disclosure. Best practices, such as minimizing data collection, implementing strict retention policies, and providing cybersecurity training, are essential. Embracing a “security in depth” strategy can further safeguard data while leveraging the potent capabilities of AI. Companies can thus harness AI without compromising data integrity.
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