The security operations automation conversation in enterprise Japan tends to follow a predictable arc. A vendor presents a capability that uses a cloud-hosted model for detection, triage, or response assistance. The CISO’s team asks about data residency. The vendor explains their cloud region. The room gets quieter.
This is not irrational caution. Japan’s financial institutions operate under real constraints about where their data goes, and the incidents that have shaped those policies were real incidents. APPI and the FSA’s data handling requirements create a specific compliance obligation around where personal data and behavioural telemetry may be processed. The question of whether a security automation tool requires sending alert context, log data, or behavioral telemetry to an external model endpoint is a legitimate procurement question, not a negotiating tactic.
Local LLM inference addresses this directly, but the current state of what is actually deployable on-premise, at the latency and accuracy thresholds that security operations require, is significantly behind what the marketing suggests.
My current personal work runs toward this problem from the bottom up: what can actually be run locally, on commodity hardware, at inference speeds that are useful for real-time security operations use cases. The honest answer right now is: more than people assume for certain tasks, less than vendors imply for others.
The use cases where local inference already holds up well are the ones involving structured data interpretation: parsing, correlation, and initial triage of high-volume, low-complexity signals. The use cases that still require cloud-class models (nuanced behavioral analysis, novel threat characterization) are also the cases where the data residency concern is sharpest.
That tension is not going away soon. The vendors who figure out a credible, technically honest answer to it will have a significant advantage in Japan FSI.