When Security Telemetry Becomes Reconnaissance: An Internal Pentester’s Look at AI-Powered…

By :- Joel Yesudas

Executive Summary

This paper examines how AI-powered monitoring platforms, while designed to improve visibility and threat detection, may inadvertently provide valuable reconnaissance opportunities for attackers who already possess internal access. From an internal pentesting perspective, the primary concern is not the AI engine itself, but the supporting infrastructure, service accounts, and telemetry repositories that consolidate sensitive operational intelligence

1. Introduction

Security telemetry is a critical element of network visibility in modern environments, and solutions leveraging AI-like UEBA, log analysis and automated threat detection-provide robust capabilities for around-the-clock monitoring of endpoints, servers, identity systems and cloud resources. Their primary objective: identify anomalous behavior faster than static rule-based systems.

For an internal pentester, however, these tools present a fascinating security challenge. Their unparalleled visibility over the environment-including authentication logs, asset inventory data, privileged user activity, network metadata and security alerts-is invaluable to defending the organization. The other side of this visibility coin, though, is that a fully consolidated intelligence platform can easily become a potent reconnaissance tool for an attacker that already has an foothold inside.

2. Internal Pentest Assumptions

For the purpose of this paper, we assume the attacker already has achieved some level of access within the organization, likely via compromised endpoint, stolen credentials, phishing, malware, or legitimate access abuse. Their goal is not necessarily initial compromise, but rather to:

• Identify high-value targets.
• Discover privileged accounts.
• Move laterally within the network.
• Escalate their privilege.
• Obtain sensitive data.

Most AI-powered monitoring platforms can become attractive reconnaissance targets given the sheer volume and breadth of information that these platforms aggregate.

3. Internal Attack Scenario

Consider a typical enterprise environment utilizing Active Directory, endpoint agents, a centralized SIEM and an AI-driven UEBA platform. An attacker has compromised a standard employee workstation via phishing or credentials theft and is now looking for ways to increase their foothold within the network.

Instead of attempting to directly target production servers, the attacker first investigates the monitoring infrastructure. The logic here is that the platform provides a singular point of information regarding user and system activity, administrative actions, etc. As an internal pentester, a crucial question becomes, can a centralized monitoring platform accelerate the recon process compared to traditional network enumeration techniques?

4. Understanding the Monitoring Architecture

At their core, AI-powered security platforms aggregate data from numerous sources, such as:

• Endpoint agents
• Active Directory
• Cloud audit logs
• Network devices
• Security tools
• Authentication systems

Data is ingested and analyzed to look for abnormal activity. This may improve detection capability, but from an attacker's point of view, it can appear to be a centralized database for operational intelligence.

The AI engine itself might be unassailable, but it's supported by infrastructure including service accounts, collectors and integrated data sources, that might not be as secure and could represent an attack vector.

5. Stage 1: Reconnaissance of Monitoring Infrastructure

Upon gaining access to an internal workstation, one of an attacker's first goals is reconnaissance. Specific intelligence they seek includes:

• Monitoring servers
• Log collectors
• Management consoles
• Service accounts

It is often simpler to identify monitoring infrastructure components due to the large number of systems these collectors interact with, coupled with easily predictable naming conventions for monitoring components. During an internal assessment, this infrastructure may be a more valuable source of intelligence than traditional network scanning tools.

Internal Pentest Validation Methodology

During the execution of an internal assessment, my objectives would be to:

• Identify the location of monitoring servers and log collectors.
• Enumerate monitoring service accounts.
• Assess the privilege assigned to these service accounts.
• Document where credentials/API keys are stored.
• Validate the segmentation of the monitoring infrastructure.
• Understand if there is any data that points toward or allows for lateral movement within the environment.

My goal is not to compromise the AI itself but to evaluate whether the supporting infrastructure can provide sensitive information.

6. Architecture

7. Stage 2: Exploiting Centralized Visibility

One of the main selling points of AI-powered security platforms is the consolidation of data. This telemetry typically includes:

• Login activity
• Administrative actions
• System and device inventory
• Hostnames
• User behavior baselines
• Authentication failures

With access to this collected data, the platform can serve as an internal recon database. Instead of enumerating the network through various tools, the attacker uses data that the organization has already collected.

For instance, authentication logs can highlight administrator login trends, the system and device inventory can pinpoint important servers within the environment, and the behavioral analysis can show relationships between specific users and their activity on systems. This significantly speeds up the reconnaissance process.

8. Stage 3: Using Service Accounts as Attack Vectors

One common element of AI-powered security solutions is the use of service accounts to gather information. These accounts could be used to:

• query Active Directory.
• read Windows Event logs.
• collect data from cloud environments.
• access sensitive databases.
• integrate with other security platforms.

Service accounts become particularly interesting during an internal assessment because they are typically designed to remain online indefinitely. They are also often configured with elevated permissions in the name of operational convenience.

A compromise of a high-privilege service account can provide access far beyond the scope of a standard user. It's not the AI platform itself which is risky, but the amount of trust that is afforded to its auxiliary accounts.

9. Stage 4: Telemetry-driven Lateral Movement

Finding a clear path into privileged systems can often prove difficult when conducting an internal pentest. AI security platforms may simplify this path by:

• Providing clear visibility into when administrators have accessed key systems.
• Showing the existence and relationships of Domain Controllers and important application servers.
• Outlining important backups infrastructure

Attackers can use this information to identify key targets. Reducing the guesswork can significantly expedite the movement of the attacker within the environment.

10. Internal Pentester's Key Finding

One point that's important to note when considering the security benefits of AI-powered monitoring solutions is that risk is often not centered around the AI detection engine. Rather, it's the supporting infrastructure. Because monitoring platforms consolidate information from so many various sources, they present a clear picture of the operational environment including the users, systems and administrative functions operating within it. The data contained here could be extremely valuable in providing intelligence about privileged accounts, critical servers and attack paths.

11. Recommendations

When approaching internal pentesting within environments that utilize AI-driven monitoring infrastructure, the following recommendations can help mitigate risk:

i. Principle of least privilege should be enforced on all service accounts.

ii. Where applicable, use managed service accounts instead.

iii. Segment the monitoring infrastructure from the rest of the user network.

iv. Administrative access should be restricted to the monitoring collectors and analytic systems.

v. Service account permissions should be reviewed and audited frequently.

vi. Stored credentials and secrets should be securely protected.

vii. Unusual access patterns toward telemetry repositories should be monitored.

viii. Monitoring infrastructure should be included in internal pentest engagements.

By implementing these recommendations, organizations can work to ensure their security platforms become an active component of the defense strategy rather than an information asset for adversaries.

12. Conclusion

AI-powered monitoring platforms provide organizations with unprecedented visibility into their environments, leading to improved detection capabilities. However, the highly aggregated data they collect transforms the underlying monitoring infrastructure, service accounts, and telemetry repositories into prime targets for internal pentesters.

The core finding of this analysis is that it's not the AI detection capability of the platform that's the greatest risk; it's the supporting infrastructure which carries the bulk of it. Security platforms built upon AI-driven monitoring solutions deserve the same level of protection that the organization dedicates to domain controllers, privileged user accounts, and other high-value assets.

After all, the platform that can see nearly every action taking place within an organization is a prime target for those seeking to exfiltrate sensitive data.