AI systems are susceptible to several common security risks, including:
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Contaminated Training Data: This risk involves data being intentionally tampered with by malicious entities aiming to harm an organization or unintentionally skewed when AI systems learn from untrustworthy sources.
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Supply Chain Weaknesses: These vulnerabilities emerge from dependence on pre-trained models, crowdsourced information, and insecure plugin extensions. The consequences can be biased data results, security infringements, or system breakdowns.
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Prompt Injection Weaknesses: Malicious inputs have the potential to force AI systems into revealing private information or initiating denial of service attacks.
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Data Tampering or Loss: This encompasses unauthorized access, insecure handling of output, permission-related problems, and excessive control that could result in undesirable behaviors or execution of unauthorized code.
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Generative AI Security Threats: These encompass intellectual property leakage, issues with data training, vulnerabilities in data storage, compliance hurdles, synthetic data worries, unintentional leaks and possible misuse for harmful attacks such as deepfakes or fake news.
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Access Vulnerabilities: Insecure plugins, insecure handling of output, permission-related problems and excessive control can lead to exploited privileges and unauthorized activities.
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Reputational and Operational Risks: These stem from poor AI outputs or actions that could tarnish an organization’s reputation and disrupt its operations.
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Human Supervision Deficiencies: A lack of human supervision in AI models can leave them open to data poisoning and manipulation by malevolent actors.
To counter these threats effectively, organizations are advised to adopt a zero-trust security approach featuring disciplined system separation (sandboxing), application-embedded controls implementation of data privacy safeguards and staying updated about emerging threats in the AI domain.
