From the course: CompTIA SecAI+ (CY0-001) Cert Prep
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Data redaction
From the course: CompTIA SecAI+ (CY0-001) Cert Prep
Data redaction
Data redaction is the process of removing or obscuring sensitive information from text or documents so that private details are not exposed. In AI systems, redaction plays a critical role in protecting privacy during both input and output processing. It ensures that personal information, such as names, addresses, or social security numbers, are never visible to unauthorized systems, users, or even the AI models themselves. When users submit a prompt to an AI system, a pre-processing step can automatically scan for sensitive patterns, like credit card numbers or e-mail addresses, and replace them with placeholder text, such as redacted. This keeps personal data hidden from the model while still allowing the system to perform its task. For example, if a company uses AI to analyze customer feedback, it can remove customer names before sending the data to the model. The AI still understands the context of the feedback without ever seeing private details. Data reduction is just as…
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Contents
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The AI lifecycle1m 39s
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Business alignment in the AI lifecycle1m 43s
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Data collection2m 20s
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Data preparation3m 15s
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Model development and selection2m 13s
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Model evaluation and validation2m 29s
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Model deployment and integration3m 25s
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Monitoring and maintenance3m 19s
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Manipulating application integrations4m 8s
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AI supply chain attacks2m 4s
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Insecure plug-in design2m 9s
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Insecure output handling1m 23s
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Output integrity attacks2m 8s
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Model denial of service1m 31s
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Excessive agency1m 33s
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Overreliance1m 34s
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AI hallucinations1m 4s
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Monitoring prompts and responses2m 51s
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Log monitoring4m 30s
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Rate and cost monitoring5m 1s
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Auditing for AI hallucinations3m 33s
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Auditing for accuracy3m 29s
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Auditing for bias and fairness4m 35s
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Auditing access and security compliance3m 48s
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Responsible AI5m 29s
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AI risks2m 23s
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Introduction of bias2m 37s
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Accidental data leakage2m 53s
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Reputational loss2m 11s
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Accuracy and performance of the model2m 22s
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Intellectual property risks3m 31s
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Autonomous systems2m 27s
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Shadow IT and shadow AI1m 48s
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Awareness training2m 21s
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