Episode Details
Back to EpisodesAI Poisoning the Quiet Enterprise Threats and CISSP Questions (Domain 1)
Description
Quiet failures are the ones that scare me most, and enterprise AI creates a brand-new way for them to spread. If a chatbot becomes the “trusted employee” everyone relies on, a slow drip of bad documents, outdated procedures, or deliberately manipulated data can poison decisions for months without a single red flag. We break down what that looks like in real organizations, why it differs from the Hollywood version of a hack, and how the business impact shows up as confident misinformation rather than obvious outages.
We also dig into the difference between data poisoning (deliberate manipulation) and data pollution (accidental garbage at scale), then connect it to retrieval augmented generation (RAG). RAG is powerful because it answers from your internal knowledge base, but that same knowledge base becomes the attack surface and the “source of truth” the model won’t question. I share practical steps you can take right now: audit what your AI actually trusts, map the full AI contact surface across workflows and repositories, treat the AI pipeline like an untrusted vendor, and assign a named owner for accuracy and security.
Then we shift into CISSP Domain 1 practice with exam-style questions that force real trade-offs: using annual loss expectancy (ALE) to recommend a risk treatment to the board, applying NIST RMF guidance even when controls are inherited through FedRAMP, handling an ethics dilemma under the ISC2 Code of Ethics, spotting the biggest BCP gap when RTO and RPO targets collide with backup frequency, and explaining why HIPAA compliance does not automatically equal GDPR compliance for EU citizen data.
If you’re studying for the CISSP or you’re building security controls around AI and cloud systems, this one is built to sharpen both your judgement and your test readiness. Subscribe, share this with a friend who’s deploying AI internally, and leave a quick review so more CISSP candidates can find the show.
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