Threats
In the context of data science and artificial intelligence, threats refer to potential risks and vulnerabilities that can compromise the security and integrity of data, models, and systems. Threats can come from various sources, including malicious actors, system failures, and natural disasters. Threats can take many forms, such as cyber attacks, data breaches, model poisoning, adversarial attacks, and data drift. Threats can have serious consequences, such as loss of privacy, financial damage, reputational harm, and safety risks. Therefore, it is crucial for data scientists and AI experts to identify, assess, and mitigate threats proactively and continuously throughout the data lifecycle and model deployment. This requires a comprehensive understanding of the threat landscape, as well as the use of appropriate security measures, such as encryption, access control, monitoring, and auditing.
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