From the course: CompTIA SecAI+ (CY0-001) Cert Prep
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NIST AI Risk Management Framework
From the course: CompTIA SecAI+ (CY0-001) Cert Prep
NIST AI Risk Management Framework
The National Institute of Standards and Technology, or NIST, created its AI risk management framework to give organizations of any size a common playbook for identifying, assessing, and controlling the risks that accompany AI systems. Although this framework is voluntary and non-regulatory, it carries weight because NIST standards often become the baseline for federal procurement and, by extension, much of the private sector that sells to government agencies. The AI Risk Management Framework centers on four iterative functions that span the entire system lifecycle. Govern establishes policies, roles, and accountability, sets risk appetite, and fosters a culture that values transparency, documentation, and continuous improvement. Map helps organizations understand the context by defining a system's intended purpose, the stakeholders affected, and the potential sources of risk including data, model design, and the deployment environment. Measure collects evidence through testing and…
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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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