The function and popularity of Artificial Intelligence and Machine Learning systems are soaring by the day. AI/ML applications have significantly evolved over the past few years and have found their applications over a wide range of business sector from healthcare or our day-to-day simple tasks like drafting email and so on. AI-based security systems use ML algorithms to identify malicious activity such as phishing attempts or unrecognized malware, notifying security teams and systems, which can then respond quickly to mitigate the threat. Though its application encompasses cybersecurity, it is itself prone to various attacks.
Security vulnerabilities in AI and ML systems can arise from various sources and can manifest in different forms. It involves adversarial attacks, data poisoning, model Inversion, model Stealing, Privacy Concerns, Biased training Data, model Decay all these may lead to unexpected behavior, incorrect predictions, biased decision, data privacy concerns, intellectual property theft and unauthorized replication of proprietary models.
Addressing these security vulnerabilities requires a holistic approach, at SecurityBoat we provide such services which includes rigorous testing, robust validation of input data, ongoing monitoring, and the implementation of secure development practices. Additionally, we prioritize transparency, fairness, and ethical considerations in AI/ML deployments to mitigate potential risks. Regular updates and maintenance are crucial to staying ahead of evolving security threats.
Threat detection involves evaluating the security ecosystem to find any weaknesses that can be exploited by adversaries.
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