Categories: AI Security and Privacy

AI Security

Definition: protecting AI systems from being misused, manipulated, or turned into an attack surface — classic security discipline applied to the new channels AI opens.

What's involved:

1. Prompt injection (direct and indirect) — untrusted content treated as instructions
2. Least-privilege tools — the runtime can only do what the task needs
3. Human confirmation gates — consequential actions need a person
4. Untrusted-content handling — delimit and label everything the model reads
5. Output/action validation — code checks what the model wants to do, outside the model
6. Content safety filtering — screening harmful inputs and outputs
7. Audit logging of every AI invocation — who, what context, what came out
8. Data exfiltration prevention — prompts and tool results are egress paths

The opener: "This is an AI-security question, and I treat it like any untrusted-input problem: least privilege first, then input handling, output validation, and audit. The AI-specific twist is prompt injection — let me walk through the layers…"

Cite it when: the question involves attacks, misuse, untrusted content, what an AI is allowed to do, or "what could go wrong" with agents and tools.

Privacy and Data Protection

Definition: controlling what data enters AI systems, where it's processed, and who can see it — driven by data classification, which decides everything downstream.

What's involved:

1. Data classification — public / internal / sensitive / federally protected (FERPA-class education records, background-check data)
2. Classification decides model routing — protected data → government tenancy with contractual terms, or local inference; public data → commodity services
3. PII minimization at the prompt boundary — the narrowest context that works
4. Retention and access policy — for uploads, logs, and indexes, decided up front
5. Permission-aware retrieval — ACL filters in the query, never prompt-level secrets
6. Approved procurement paths — vendor agreements before sensitive data flows
7. Consent and transparency — people know what the system does with their data

The opener: "Privacy-first: the starting move is data classification, because the classification decides where the model runs, what enters prompts, retention, and access. For this data, that means…"

Cite it when: the question involves personal data, transcripts, records, uploads, "can we send this to an AI vendor," or where processing should happen.

The pairing move

Security and privacy chain when the scenario has both an adversary and sensitive data — most public-facing AI does: "Two protection layers here: privacy — classification-driven routing and PII minimization — and security — least-privilege tools, injection handling, and full audit." Say the pair, then pick the one the question emphasizes and go deep.