Cribl for Security: The Control Plane for AI's Data Tsunami

I bet that you have heard about AI's promise is to make better decisions, faster. However, what comes along with AI is the hidden cost of data. 

As organizations implement large language models (LLMs) into their products, introduce retrieval-augmented generation (RAG), and roll out agents at the edge, they are generating a new class of telemetry. Including prompts and responses, embeddings, token traces, safety signals, content-filter results and lineage records. The AI exhaust stacks on top of the current applications, security telemetry and networks. 

The result? 

Storage grows faster than user adoption, storage fees creep up, licenses stretch, and the blast radius for risk widens. 

The question isn't whether AI will increase your data, it's whether you can control the increase, so insights go up while costs and risk go down. This is where Cribl comes in. Cribl provides a control plane for observability and security data that lets you route, shape, enrich and govern data in motion and then search it in place without paying the hot-storage tax. Instead of being forced to "ship everything everywhere," you decide what to keep, where it should live, and how it's protected.

The AI telemetry tsunami 

AI workloads generate data in places that you didn't have to monitor previously. Commonly called model exhaust, every LLM call leaves a trail including prompts, responses, token counts, embeddings, latency, and error details. In vector stores and RAG, there are index builds, reindex churn, document metadata, and similarity search logs. Now that we have AI implemented in some way in most of our pipelines, classic application and infrastructure telemetry doubles. Leaving these streams unmanaged will overwhelm indexers and SIEMs, potentially even inflating cloud bills. More importantly, unfiltered prompts and responses can send secrets or PII into destinations where they don't belong. 

Principles for AI era data 

There are three principles that separate resilient AI data strategies from reactive ones. The first is selective capture. Keep what drives decisions and drop or summarize the rest before it hits expensive tools. The second is policy-first pipelines. You can do all of your data clean-up and protection in one place, mask secrets, replace direct identifiers with a consistent fingerprint, enforce retention, and keep a clear record of what was changed, when, and by which rule, so you can prove compliance later. 

Finally, freedom to choose tools. Route data producers to the right destinations today and new ones tomorrow without rewriting data producers or agents. If you can't do these three things, every AI rollout will become a tax on your data platform. 

Cribl as a control plane for AI data 

Cribl Stream is the smart switchboard for data in motion. It ingests virtually anything, transforms events in transit, and routes them to multiple destinations simultaneously. Route one input to multiple outputs. Send full fidelity data to object storage while dispatching curated summaries to premium analytics. Shape the data by dropping noisy fields, sampling low-value traffic, deduplicating heartbeats, flattening nested JSON and normalizing LLM trace formats. Enhance the data by incorporating context from configuration management databases (CMDBs) or inventories, allowing downstream analytics to gain meaning. Mask prompts and responses with regex policies, tokenize PII and enforce naming schema conventions centrally. Offload heavy data to inexpensive object storage to keep hot tiers lean and queryable, helping to control costs. Additionally, with the ability to replay the raw archive into your SIEM with updated parsing, you can help avoid full re-ingest cycles. 

Cribl Edge pushes the control to the source, endpoints, servers, and Kubernetes nodes. This allows the data to be filtered and formatted close to where it is produced. Additionally, it allows only the necessary data to be forwarded to a different destination.  

Cribl Search allows you to run analytics directly on S3, object storage, and other data lakes without pre-indexing. This means you can explore historical prompts, embeddings, metadata and safety logs in place without waiting for pipelines. 

Metrics that matter

 At this point, you may be wondering why this matters and what numbers you can track to see how Cribl can truly help with the AI data surge. To start, you can reduce costs by removing 35-60% of fields that don't add value, so instead of sending TBs/day to expensive tools, you can send much less while keeping a full copy in low-cost storage. When usage increases during a new model launch, Cribl Stream can slow and buffer the flow, so your indexers don't get overwhelmed. A significant amount of time can also be saved, as you can search data directly in S3 without re-ingesting it, allowing investigations to start in minutes instead of hours. 

The bottom line

AI doesn't just create new insights; it creates a surge of data. If you try to manage the surge by sending all the data to high-cost destinations, you'll drown not only in cost but in complexity. Cribl turns this surge of data into fuel by providing you with selective capture, policy-first pipelines and in-place search. Allowing you to scale AI confidently, prove governance and keep your options open. With Cribl, collecting everything becomes collecting smart.