Inference-service data boundary
In one sentence how long it's retained, how long abuse-monitoring logs are kept, how enterprise vs. consumer tiers differ, whether there's zero data retention (ZDR), who the subprocessors are, which region the data lands in, and whether there's a DPA/BAA. And remember: these terms change, and can be overridden by a legal order. Treating one line — "we don't train on it" — as the whole boundary is the most common operational-phase false security.
Data lifecycle & deletion propagation
In one sentence govern by "data lineage + deletion propagation," and don't read "deleted from the primary DB" as "deleted" — that's the most common false security here.
Fine-tuning-as-a-service privacy
In one sentence how long it's retained, whether it's used for training or human review, whether the resulting fine-tuned model is yours alone — none of this is a single number; verify each vendor's current terms item by item (most vendor docs say fine-tuning inputs/outputs aren't used for training by default and the fine-tuned model is for your use alone — but still check the terms for your tier, your endpoint, your region, and remember they change). Face 2 — fine-tuning itself erodes alignment handing data to a vendor fine-tuning API means you must both verify the data boundary and assume fine-tuning will weaken alignment — including the privacy refusals the model would otherwise make. (Note: Qi et al. is primarily a result about safety-alignment erosion, not a direct data-leak / PII-extraction result — this entry uses it to say "fine-tuning weakens privacy refusals along with the rest," and does not overstate it as "fine-tuning can extract the training data.")
Synthetic-data privacy
In one sentence synthetic data provides no anonymity by default. Stadler et al. (USENIX Security 2022) showed that any privacy evaluation based on "how similar real and synthetic records look" severely underestimates risk, and that synthetic data is no safer than traditional anonymisation — unless generated under a formal differential-privacy (DP) guarantee; and even with DP, you can't escape a hard privacy↔utility trade-off. Chen et al. (GAN-Leaks, CCS 2020) go further: even when the attacker can only sample from the generator (full black-box — exactly the "publish a synthetic dataset" case), membership inference (MIA) still distinguishes whether a given real record was in the training set above chance, and gets more accurate as overfitting rises. The only thing that gives a bound is formal DP, and ε is not zero.
LLM watermarking & provenance
In one sentence one paraphrase with enough budget wipes the signal out. Kirchenbauer et al.'s (ICML 2023) green/red-list watermark gives interpretable p-values from a z-test and needs no model access to detect, but detectability trades against text entropy (low-entropy / short outputs are harder to mark); their follow-up reliability study (ICLR 2024) measured that human and especially LLM-based paraphrase markedly lower detectability, the watermark gets "spread out," detection needs more tokens, and WinMax + SelfHash only partially recover. Conclusion first: a watermark is probabilistic evidence for provenance / forensics, not a strong guarantee — don't read "watermarked" as "can't be removed."
Persistent memory privacy & retention
In one sentence how much you can see and delete is decided by the product and its backend. The harder lesson comes from NYT v. OpenAI (2025) govern the product's memory as one more copy of your data, subject to both the product and the law — don't read "I flipped the switch off" as "it's all deleted."