Financial services IT and security operations centers (SOCs) face a constant influx of system alerts, support tickets, and potential security events. While many firms are turning to artificial intelligence agents to investigate these queues, the sheer volume of data can quickly drive up large language model (LLM) token usage and processing costs. A recent tool targets this specific bottleneck: according to a November 2026 report, Kindo introduced Deep Hat Sift to filter enterprise alerts before they ever reach an AI agent.
The operational problem for IT directors is that an alert surge—such as a wave of reported phishing emails—can trigger thousands of potential AI agent runs. Sift is designed to sit between the incoming event stream and the Kindo AI agent to manage the cost of deciding which events warrant actual AI processing. Sift learns which types of events the agent typically dismisses. Once a human operator approves a confidence threshold, the system withholds similar low-priority events from the agent's review queue while maintaining a log of every automated decision.
Evaluating and Implementing AI Alert Filtering
Before an IT or security team can deploy this type of filtering in a live environment, they must complete several foundational steps. The underlying security or ticketing system will continue to produce the raw events, meaning the integration between the event source and the filtering layer must be highly reliable. Moving from an observational "shadow mode" to active live filtering requires deliberate human oversight.
If your financial services organization is considering a similar pre-filter for AI agents, your deployment team should prepare for the following setup prerequisites:
- Data source pairing: Each filter must be configured for a specific incoming data source, a designated AI agent, and a defined task (e.g., reviewing a reported-phishing queue).
- Human review sets: Teams must define what should reach the agent by establishing a review set of approximately 50 events drawn directly from the source.
- Threshold approval: Human reviewers must judge the initial examples so the system can learn what to safely discard before applying filters autonomously.
Designing workflows that route high volumes of ticketing data through AI safely requires specialized planning. A consultancy experienced in AI and data intelligence can help operational leaders map their existing security and ticketing event streams, establish secure data pipelines, and structure the human-in-the-loop review processes necessary for triage tools to function accurately.
If your IT operations team is struggling to balance alert triage volumes with the rising cost of running AI agents, we can help you evaluate your current infrastructure. To discuss how to optimize your event streams and AI workflows, reach out to our team at Decision Intelligent.