From the course: CompTIA SecAI+ (CY0-001) Cert Prep
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Incident response ticket management
From the course: CompTIA SecAI+ (CY0-001) Cert Prep
Incident response ticket management
The first formal step in managing the response when a security incident arises is normally creating a ticket in the organization's Incident Tracking System. This ticket becomes the central record for tracking the incident from discovery to resolution. It typically includes a description of the issue, supporting evidence, affected systems, assigned personnel, steps taken, and any lessons learned. Keeping tickets complete and up-to-date is critical for both operational success and audit readiness, but it can also be tedious and time-consuming, especially during fast-moving incidents. AI helps streamline this process by assisting with ticket creation, enrichment, correlation, and ongoing updates. It can automatically populate tickets with information pulled from alerts, logs, and threat intelligence. It can also detect when multiple alerts are part of the same incident and merge them into a single case, reducing duplication and confusion. As the incident unfolds, AI can suggest…
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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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