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19/08/2026 3:25 pm
TL;DR: Compression and language modelling are both prediction problems, with models estimating symbol probabilities and entropy coders turning those probabilities into compact bitstreams, according to ngrok’s analysis. The practical lesson is that better prediction lowers encoding cost, which is the same mathematical pressure shaping modern LLM training and evaluation.
NHIMG editorial — based on content published by ngrok: the relationship between compression and language modelling
Questions worth separating out
Q: How should organisations reduce information exposure when using LLMs on internal data?
A: Start by minimising the data that reaches the model, then constrain retrieval to the smallest task-relevant context.
Q: Why does better prediction make compression and language modelling look similar?
A: Both systems assign probabilities to possible next symbols.
Q: What do security teams get wrong about AI and data classification?
A: They often treat classification as a labelling exercise instead of an access-control input.
Practitioner guidance
- Review what data your AI systems can infer, not just what they store Map prompts, logs, training corpora, and retrieved context for repeated structures, secrets, and identity-related records.
- Apply data minimisation before model ingestion Strip credentials, access tokens, personal identifiers, and unnecessary operational detail from datasets before they enter LLM pipelines.
- Treat model performance metrics as governance inputs Track cross-entropy, confidence, and retrieval scope alongside data classification and access controls.
What's in the full article
ngrok's full article covers the mathematical detail this post intentionally leaves at a higher level:
- Step-by-step walkthroughs of run-length encoding, arithmetic coding, and Huffman coding for readers who want the mechanics.
- Worked examples showing how symbol probabilities translate into bit savings across different compression methods.
- A clearer explanation of entropy, cross-entropy, and why predictive models improve compression efficiency.
- Interactive illustrations and derivations that show how a probability model is turned into a compressed bitstream.
👉 Read ngrok’s full explanation of why compression and language models share the same maths →
Compression and language models: what practitioners should take away?
Explore further
19/08/2026 4:10 pm
Prediction is the shared primitive behind compression and LLM behaviour. The article is correct to frame both systems as probability machines, because each one assigns weights to possible next symbols and pays a cost when the guess is poor. That matters for governance because models are not neutral containers for data, they are inference engines that extract structure from inputs. In identity and AI programmes, that means sensitive context can be transformed into reusable probabilistic patterns, which changes how teams should think about exposure and retention.
A question worth separating out:
Q: How do IAM and data security teams align on AI governance?
A: They should align around the same control objective: explainable access to sensitive data. IAM teams own entitlements and identity review, while data teams own classification and lineage, but AI risk emerges where those controls overlap. The best programmes treat access path visibility as a shared requirement.
👉 Read our full editorial: Compression and LLMs share the same prediction problem
