A controlled study using four network telemetry sources and multiple LLMs shows that high-quality, protocol-rich security data such as Corelight logs improve core investigation metrics…
Dangerous AI errors often arise not from technical failures, but from hidden data problems and model deviations that only become visible once business-critical decisions have…
RL environments with software bugs (stale cache, reward hacks, false state transitions) generate toxic training data that sabotage agent training – systematic quality validation is…
STRIDE formalizes training data attribution as a sparse recovery problem in activation space, achieving an order of magnitude faster results than gradient-based methods.
