08/31/2026
Every model is only as trustworthy as the it learns from — and most organizations are quietly starving their models by feeding them summaries instead of the real thing.
In our latest blog post, we break down why pre-processed and flow records strip out the very anomalies AI needs to learn from ("Anomalies, by definition, don't fit that model"), why raw data preserves the optionality to ask new questions when threats emerge, and how the split between training and production inference data quietly degrades model performance over time.
The fix isn't hoarding everything forever at hot-storage prices, it's a tiered hot/warm/cold architecture that makes petabyte-scale raw data retention practical.
Read the full breakdown:
Learn how petabyte-scale mission data management, tiered storage, and native replay improve ISR, cybersecurity, and defense operations while reducing costs.