CISOs and security teams are racing to put AI agents into the SOC. In fact, a recent AWS survey of 2,800 tech and security decision-makers found that 38% plan to deploy AI agents in the SOC over the next year. And Gartner recently reported that 42% of cybersecurity leaders are already piloting AI agents for threat detection and response capabilities.
Pushed by C-Suites that want to implement AI across their organizations and get on equal footing with cyberattackers already harnessing AI, the promise of agentic in SOCs is intoxicating. They show the potential to compress mean-time-to-triage, tame alert floods, and give human analysts time back for high-value work.
But amid the rush, a simple truth keeps getting lost: AI agents are only as good as the data they’re fed. If your telemetry is fragmented, your schemas are inconsistent, or your context is missing, you won’t get faster responses from AI SOC agents. You’ll just get faster mistakes. These agents are being built to excel at cybersecurity analysis and decision support. They are not constructed to wrangle data collection, cleansing, normalization, and governance across dozens of sources.
What “Good Data” Actually Means for SOC Agents
Before worrying about how to provide SOC agents with good data, you first need to understand what good data actually is. When people hear “good data,” they jump straight to quality. Quality matters, but for AI agents in the SOC, good data has three properties:
- Accessible: Agents need low-latency, permissioned access to a complete body of evidence. Suppose the data required to resolve a security alert is stored across six product APIs, each with rate limits, custom authorization, and bespoke query languages. In that case, the agent will spend its compute budget negotiating plumbing rather than analyzing risk.
- Normalized: Modern SOCs integrate telemetry from EDRs, cloud providers, identity, networks, SaaS apps, data lakes, and more. Normalizing all that into a common schema eliminates the constant “translation tax.” An agent that can analyze standardized fields once, and doesn’t have to re-learn CrowdStrike vs. Splunk Search Processing Language vs. vendor-specific JavaScript Object Notation, will make faster, more reliable decisions.
- Clean and Enriched: Through both automated and manual processes, data errors must be identified and corrected before being sent to the agent. Once the inconsistencies and inaccuracies are addressed, the data also needs to be enriched with background information (such as its source) to ensure reliable AI-powered analysis.
Making More ‘Good Data’ Available in a Central, Accessible Store
While “security-relevant” data is most important to your human cybersecurity analysts, AI SOC agents perform far better with more data and the whole body of evidence. The catch is where that evidence lives.
If the agent must “crawl back” into five source systems to enrich an alert on its own, latency spikes and success rates drop. The right move is to centralize, normalize, and clean security data into an accessible store, like a data lake, for your AI SOC agents and continue streaming a distilled, security-relevant subset to the Security Information and Event Management (SIEM) platform for detections and cybersecurity analysts. Let the SIEM be the place where detections originate; let the lake be the place your agents do their deep thinking.
The problem is that the industry’s largest SIEM, Endpoint Detection and Response (EDR), and Security Orchestration, Automation, and Response (SOAR) platforms are consolidating into vertically integrated ecosystems. Many of which now offer native agentic features. Agents that sit outside these ecosystems often face throttled or uneven access, making it impossible to rely on consistent, high-volume machine querying. You cannot operate a modern, AI-native SOC around the hope that “maybe the API limits hold this week.”
A Smarter Approach to Governing AI Agent Data Access and Control
At the same time, data governance cannot be an afterthought in SOCs with agentic AI. As agents gain more reach, access must become more, not less, tightly controlled. Sensitive fields need to be redacted or tokenized before they leave their source systems.
Agents should receive only the datasets they need for explicit purposes, not universal read-all visibility. Provenance must follow the data so decisions can be audited, and regulatory obligations such as HIPAA, PCI, and GDPR have to be enforced in the pipeline rather than bolted onto the agent layer.
The answer to this challenge is the same as the accessibility challenge: a vendor-agnostic, policy-enforced security data fabric that extracts, normalizes, and routes telemetry once into a central, machine-efficient evidence store. This is the only sustainable way to give agents consistent access while preserving the governance controls enterprises depend on.
Data First, Agents Second
AI agents will change how SOCs work, but they won’t save a broken data foundation. If your telemetry is siloed, your schemas are inconsistent, or your context is missing, you’ll automate noise, not insight. There needs to be investment in the data security layer in areas such as collection, cleansing, normalization, and governance, so your agents can focus on analysis and decisioning rather than data plumbing. Give them accessibility, consistency, and context, and they’ll even the playing field against AI-enhanced cyberattackers.
Furthermore, you’ll ensure your SOC moves not just faster, but in the right direction.
