AI Safety & Data Privacy

IBM Technology. Explains the ingestion side of RAG and agents: preparing PDFs and other files so document structure, tables and layout survive into downstream retrieval. That supports the article's warning that RAG quality and safety begin before embedding, especially when parsing complex business documents.

Paragon. Walks through the production RAG permission problem and compares tool-calling, namespaces, ACL tables and relationship-based permissions. That directly supports the article's core rule: retrieval must only return sources the current user is allowed to see, and source-system permissions cannot be treated as an afterthought.

Arize AI. Explains why production agents fail when the system lacks the right context, evaluation data, tracing and domain expertise. It maps well to the article's failure-mode register because it makes reliability an engineering loop: separate retrieval from reasoning, define expected outcomes, evaluate tool calls, and trace failures before changing models.

GOTO Conferences. Connects the EU AI Act to data quality, MLOps, documentation and post-deployment monitoring. That makes it a good companion for the article's SME governance baseline: the work starts with knowing the system, data, owner, purpose and controls, not with buying a compliance platform.

RAG quality starts before retrieval. A secure ingestion guide for PDFs, OCR, metadata, permissions, source freshness, deletion, malware risk, and operational ownership.

A company knowledge assistant is only safe if retrieval respects permissions. How to design RAG source boundaries, ACL filtering, document ownership, logging, stale-source handling, and refusal behavior.

AI systems usually fail in predictable ways: hallucination, stale context, sycophancy, prompt injection, unsafe tool use, schema drift, and weak fallbacks. A production failure-mode register for teams shipping real workflows.

The EU AI Act is not just a legal problem for large vendors. A practical SME plan for inventory, risk classification, human oversight, transparency, vendor records, and rollout discipline.

Prompt injection is a permanent LLM security class, not a prompt-writing mistake. A production guide to threat models, data boundaries, tool permissions, regression tests, monitoring, and incident response.

Connecting AI to your real tools — email, calendar, CRM — is the productivity unlock and the risk. A practical guide to the integrations that work in 2026, the patterns that are safe, and the lines you should not cross.

A practical guide to using AI at work without accidentally exposing customer data, breaching your company's policy, or violating GDPR. The lines, the tools, and the habits to build.

An honest look at what AI assistants actually do with your data — what is stored, what is used for training, what the privacy settings really mean, and the three changes worth making today.

LiveOverflow. Walks through the actual defence-in-depth playbook — taint analysis on LLM output, restricting expected output shapes, user isolation, few-shot scaffolds, fine-tuning, temperature 0 for determinism, redundancy for critical paths. It matches the article's defence-stack section almost item for item.

LiveOverflow. Frames prompt injection as a classic injection attack against systems that mix instructions and untrusted data — with a concrete content-moderation example where an attacker frames an innocent user. The mental shift from "the model is the target" to "the application is the target" is exactly the move the article opens with.

IBM Technology. Zooms out from prompt injection to the wider OWASP Top 10 for LLMs — insecure output handling, sensitive information disclosure, excessive agency — which is exactly the failure-mode catalogue you want in mind before you grant Gmail or HubSpot scopes to anything.

IBM Technology. Jeff Crume's "buy an SUV for $1" example is the cleanest 10-minute explanation of why direct and indirect prompt injection are different problems, and why filtering can't fully solve either. It pairs directly with the article's argument that you need least-privilege scopes, a dedicated agent account, and a human in the loop on anything irreversible — not a cleverer system prompt.

IBM Technology. Sits below our usual 100K bar but earns the slot because it's the single best short explanation of why an employee using a personal ChatGPT account on work problems is the actual risk most companies face. Crume's "don't say no, say how" framing is the same posture the article takes — you're not trying to ban AI, you're trying to make safe use the easy default.

IBM Technology. Jeff Crume's lightboard explainer of the three places generative AI introduces risk — the data, the model, and the usage — and what good controls look like for each. Useful for the article's argument that "be careful" isn't enough; you need to think about which category of risk you're actually exposed to as an employee.

Theo Von. The section roughly twelve minutes in, where Altman admits there is no legal privilege for ChatGPT conversations and that OpenAI can be ordered to hand them over in a lawsuit, is the single most-quoted piece of footage on this topic — and worth hearing in his own voice rather than via a news clip. The rest of the conversation is wide-ranging, but that one exchange is the honest answer to the question the article asks: "what does the company actually do with what I type?"