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Your operations run on log data silos. Splunk for the SOC. Datadog for observability. ServiceNow for ITOps. Separate tools for the NOC, compliance, and DevOps. Each team built or inherited their own logging infrastructure, and each system became a walled garden. Your team has to spend time filling in the gaps between silos manually.
Now, vendors are selling you "agentic" add-ons for each data silo. AI copilots for your SIEM. Autonomous investigation for your EDR. Intelligent alerting for your observability platform. The pitch sounds reasonable: make each tool smarter, and your overall operations improve. But organizations use an average of 45 cybersecurity tools, adding agents that can’t see across data sources is a problem.
Here's what actually happens: you end up with smarter silos that still can't talk to each other. AI can’t fill in the gaps with manual access and shared insights like your human team can,so they end up trapped with the blind spots your team has been working around for years.
Each tool you "agenticize" might look like it’s seeing efficiency improvements at first. But if none of them can see the full picture, problems quickly start to arise.
The SOC agent notices suspicious authentication patterns but can't check whether network traffic supports the hypothesis. That data lives in the NOC's tools. The observability agent detects a performance anomaly but can't determine whether it's an attack or a misconfiguration. Security logs are locked in the SIEM. The compliance agent flags a policy violation but can't correlate it with the change that caused it. That information sits in the DevOps pipeline.
Each agent is individually competent and collectively blind.
Here's where the economics get painful.
According to the SANS 2025 Detection & Response Survey, 73% of organizations list false positives as their number one challenge in threat detection. When an incident spans multiple systems, your agents don't coordinate. They investigate the same event independently, each from their limited vantage point. The SOC agent creates an alert. The NOC agent creates another alert. The observability agent flags the same underlying issue a third time. The compliance team gets notified separately.
Four tools. Four agents. Four alerts for one incident.
Your teams now spend time correlating AI-generated alerts instead of the raw data. The automation didn't reduce alert fatigue. It multiplied it across more sophisticated sources.
Storage costs compound the same way. Each silo maintains its own log retention, its own indexes, its own duplicated data. When you added agents, you likely increased retention requirements because AI models need historical context for pattern recognition. You're now paying for expanded storage in every silo, not just one.
When agents can only see one silo, they can only take action within that silo.
Consider an incident that starts with a misconfigured deployment (DevOps logs), causes a performance degradation (observability), triggers security alerts (SOC), violates an SLA (ITOps), and creates a compliance gap (audit logs). A useful response requires coordinated visibility and action across all of these systems.
Your observability agent can't roll back the deployment. Your SOC agent can't see the performance data that would rule out an attack. Your compliance agent can't access the operational context that would explain the violation. Each agent is stuck in its lane, capable of partial analysis that doesn't address the full picture.
Manual coordination becomes the fallback. Teams receive fragmented AI recommendations from each system and piece together the complete view themselves. The agents accelerated specific steps while leaving the critical cross-domain correlation to humans.
Instead of adding AI agents to separate data silos or trying to force centralization of logs, a unified visibility layer works with the reality of distributed data.
The architecture works by querying the data where it is now. Strike48 connects to your existing systems—Splunk, Datadog, Snowflake, S3, or wherever your logs live—to provide unified visibility without the need for centralization. Data can stay where it is or be centralized. Agents can see all of it, providing the cross-platform context needed for AI to move from chatbot to an automated team member.
When an agent investigates an incident, it queries across all connected sources simultaneously. Cross-domain correlation becomes possible because agents see the full picture. Agents can then connect the misconfigured deployment with the performance degradation, security alert, and the compliance gap to surface the root cause that spans all of them.
This approach changes the cost structure so you stop paying for expanded storage in every silo to store the same log multiple times. It also allows you to stop paying for agent add-ons on each individual tool. And it saves you from having to pay teams to painstakingly correlate AI-generated alerts from disconnected systems manually.
Adding AI to each silo optimizes the pieces while missing the whole. Unified log visibility means your agents see everything, correlated across domains, enabling them to take action like a team member.
Learn more about Strike48’s architecture to see how agents investigate across your SOC, NOC, ITOps, and observability data for complete visibility and cross-domain correlation.