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README.md

@@ -82,6 +82,63 @@ checkpoint. Reference behavior:
 | Real SSH login attempt | ~12–16 | borderline |
 | Random gibberish / injected text | >100,000 | anomaly |
 
+## Applications & use cases
+
+Because the model learns the "grammar" of normal system logs, it can support a
+range of log-analytics and security tasks:
+
+- **Security monitoring / intrusion detection** — flag suspicious log lines
+  (brute-force SSH attempts, injected commands, malformed requests) that don't
+  match learned normal patterns. This is the core "sentry" use case.
+- **Operational anomaly detection (AIOps)** — surface rare error conditions,
+  misconfigurations, or failing components before they escalate, by ranking log
+  lines by how "surprising" they are.
+- **Log triage & noise reduction** — instead of reading thousands of lines,
+  engineers review only the top-scoring (most unusual) ones.
+- **Log autocompletion / templating** — suggest or complete log-line formats given
+  a prefix (useful in tooling and for synthetic log generation for testing).
+- **Data-quality / format validation** — detect corrupted, truncated, or
+  non-conforming log entries in an ingestion pipeline.
+- **Education** — a compact, readable reference for how a GPT-style Transformer,
+  tokenizer, and perplexity-based anomaly scoring work end to end.
+
+### How it can be used
+
+- **Batch scan a log file** — score every line and export the anomalies:
+  `python detect_anomalies.py --file /var/log/auth.log`
+- **Score individual lines** programmatically via `score_line(...)` and compare
+  against the saved `threshold` (see the usage snippet above).
+- **Stream integration** — call the scorer inside a log shipper (Fluent Bit /
+  Logstash / a Kafka consumer) to tag or route high-perplexity lines in near real
+  time.
+- **Alerting** — wire flagged lines into Slack/email/PagerDuty or a SIEM
+  (Splunk, Elastic) as an enrichment signal.
+- **Retrain on your own logs** — point `LOG_FILE` at your data and run
+  `train_and_generate.py` to specialize the model to your environment.
+
+## Future scope
+
+Natural next steps to make this research prototype more capable and production-ready:
+
+- **Per-source models & thresholds** — separate models for SSH, Apache, kernel,
+  etc., removing the single-global-threshold weakness.
+- **Session / sequence-level modeling** — detect anomalous *sequences* of events
+  (e.g. login → privilege escalation → data access), not just single lines.
+- **Larger context & scale** — bigger `d_model`, more layers, longer
+  `max_seq_len`, and training on full Loghub datasets (millions of lines).
+- **Template-aware tokenization** — parse logs into templates + variables (à la
+  Drain) so IPs/IDs don't inflate the vocabulary or the anomaly score.
+- **Calibrated scoring** — convert raw perplexity into probabilities/severity
+  levels, with per-line-position attribution ("which token was surprising?").
+- **Real-time serving** — expose the scorer as a REST/gRPC service or a Hugging
+  Face **Space** (Gradio demo) for interactive use.
+- **`transformers` integration** — wrap as a `PreTrainedModel` so it loads via
+  `AutoModel.from_pretrained` and plugs into the standard ecosystem.
+- **Evaluation & benchmarking** — measure precision/recall on labeled anomaly
+  datasets (e.g. BGL, HDFS) against baselines like DeepLog and LogBERT.
+- **Explainability & feedback loop** — let analysts confirm/dismiss flags to
+  continuously refine the threshold and reduce false positives.
+
 ## Limitations
 
 - Trained on a **small, mixed** corpus (three log sources). A single global