Preparing Your Autonomous Security Agents: Ensuring They Have All the Data They Need for Success

Endpoint agents lack the capability to report their own absence, creating a significant challenge for Security Operations Center (SOC) teams. According to the 2026 Axonius Actionability Report, a joint effort with the Ponemon Institute that involved 662 IT and security professionals, a concerning statistic emerged: 12.7% of devices across a median inventory of 298,000 are without their expected security agents.

When a device is agent-less, it becomes invisible to management consoles, and outdated records in Configuration Management Database (CMDB) can lead to missed reconciliations. For instance, an employee may create a SaaS workspace using Claude Enterprise without going through the proper channels, thus leaving a gap in telemetry data that fails to account for all assets. Consequently, EDR dashboards can provide misleading coverage percentages since the reporting cannot identify what it doesn’t see.

This issue has grown more critical in recent months as SOC and Extended Detection and Response (XDR) vendors increasingly implement autonomous investigation and remediation measures. These agents rely on the same dashboards and metrics as human analysts. While a human might question a 98% coverage statistic, autonomous agents trust it as factual, operating at machine speed.

Three Signals Highlight the Visibility Gap

A survey by Gravitee involving over 900 executives revealed that 88% had experienced AI-related incidents, but only 14.4% had deployed agents with full security approval. The Axonius/Ponemon report indicated 52% of respondents would allow autonomous agents to act based on recommendations, yet 63% noted critical gaps in the underlying data. Furthermore, the Cloud Security Alliance’s Agentic Trust Framework necessitates verified governance of data before agents can operate based on any findings.

Mike Riemer, Field CISO at Ivanti, mentioned that known vulnerabilities within Azure’s honeypot networks are being attacked in under 90 seconds, stressing the continuing need for traditional security measures, albeit with the caveat that these measures only protect visible assets. An EDR agent covering 87.3% of an inventory leaves 12.7% of devices unmonitored, missing from policy enforcement and detection frameworks.

Evidence of Coverage Issues

Joe Diamond, CEO of Axonius, stated that most Chief Information Security Officers (CISOs) only see about half of what’s on their network, describing the remaining assets as “dark matter.” Data from over 900 Axonius clients backs this finding. For example, TransUnion improved its endpoint coverage from 70% to 99% through out-of-band verification, while Western Union increased theirs from 85% to 99% by consolidating input from 38 different tools. Lumen uncovered 1.1 million assets when their CMDB reflected just 17,000. This translates to approximately 37,000 unmanaged endpoints per organization, which fall outside any enforcement protocols or patch cycles.

Diamond draws attention to Anthropic’s Mythos, suggesting that enhanced machine-speed capabilities could exacerbate risks posed by unknown assets. He warns against rushing to granular AI governance without first fully understanding existing environments, describing this transition as potentially monumental, rivaling the internet’s inception.

Three Strategies to Address Visibility Challenges

Currently, there is no single solution to the visibility issue; three different strategies are vying for adoption. Each has its trade-offs that security teams should assess.

A Dedicated Integration Layer employs bidirectional API adapters to maintain a current inventory. Axonius, for instance, has over 1,400 adapters and can detect hidden Claude Enterprise installations through its Anthropic adapter which became generally available on June 15. “We have established a bidirectional API integration across all IT systems and security controls to sustain an always-current inventory,” said Diamond.

Platform-native EDR & XDR Intelligence provide deeper context by leveraging intelligence from within the agent footprint. While this method offers an advantage in depth, it’s limited by the visibility of the agent itself, especially in areas identified as gaps by the Ponemon report.

CMDB Modernization involves continuous reconciliation with three or more independent telemetry sources. According to data from Axonius and Ponemon, only 13% of organizations perform daily reconciliations, leaving the remaining 87% reliant on outdated records that complicate automated remediation efforts.

Evaluating EDR Data Readiness for Autonomous Remediation

Before granting autonomous SOC agents authority to close tickets or quarantine assets, it’s vital to assess whether your EDR and asset data is reliable enough. This assessment is vendor-neutral and applicable across any EDR and CMDB, consisting of five critical checkpoints that can be addressed in a single session.

Key Questions Before Allowing Autonomous SOC Decisions

1. What independent method verifies endpoint-agent coverage beyond the EDR console?

2. How does the SOC resolve discrepancies among EDR, CMDB, cloud inventory, IdP, and other discovery tools?

3. Can AI agents work with assets whose ownership is unknown or debated?

4. Can the system differentiate between “not vulnerable” versus “not visible”?

5. What data-quality metrics will inhibit autonomous remediation if gaps or ownership issues arise?

Communicating Risk to the Board

Kayne McGladrey, an IEEE Senior Member, has noted that the lack of self-reported coverage accuracy is an ongoing issue. However, the advent of autonomous agents acting at machine speed introduces new risks, as these systems bypass the manual checks that human analysts developed over time. According to Diamond, “Findings accumulate because the data isn’t trusted, and absent ownership leaves entire asset classes off the radar.”

The CSA’s Agentic Trust Framework mandates that any agent given greater autonomy must meet five criteria, including accuracy and passing security audits. Furthermore, the EU AI Act’s transparency requirements are set to come into effect on August 2, 2026, while higher-risk system obligations will be enforced by December 2027. Organizations that deploy autonomous SOC agents based on incomplete data face immediate risks that could outstrip any regulatory timelines.

A crucial takeaway for the board could be: “Our EDR coverage assessments are fundamentally flawed because an endpoint agent cannot indicate its own absence, and we are actively verifying coverage through out-of-band methods before deploying autonomous agents, which would make decisions based on those reports at machine speed.”

Security Director’s Action Plan

1. Conduct out-of-band asset discovery this week. Compare findings against your CMDB and EDR counts. If discrepancies surpass 10%, pause automated remediation until resolved.

2. Implement SaaS discovery for AI services. Since employees may adopt AI prematurely, regular weekly scans are essential, directing unmanaged high-risk instances to your incident response queue for prioritization.

3. Ensure ownership mapping correlates with remediation duties. As found by Ponemon, inconsistencies hinder automated processes. Fix ownership discrepancies before implementing agents reliant on this data.

4. Avoid relying solely on self-reported metrics. Any risk assessment based only on EDR-reported data should be disregarded. Require external verification for all coverage statistics impacting risk decisions.