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Introduction:
The managed detection and response (MDR) landscape is undergoing a seismic shift, moving beyond traditional human-led monitoring to AI-driven, automated security operations. CrowdStrike’s recognition as a Leader in The Forrester Wave™: Managed Detection and Response Services in Europe, Q3 2025, underscores a new era where artificial intelligence is central to threat hunting, detection, and response playbook generation, fundamentally changing how organizations defend against advanced threats.
Learning Objectives:
- Understand the core AI and automation technologies powering next-generation MDR services.
- Learn practical command-line and tool configurations for enhancing endpoint visibility and threat hunting.
- Develop skills to implement AI-augmented security controls and mitigate modern attack vectors.
You Should Know:
1. AI-Enhanced Endpoint Investigation with CrowdStrike Falcon
The CrowdStrike Falcon platform provides deep visibility. While its full power is cloud-based, investigators often use its data alongside local system forensics.
`cs tool-query -d –filter=”process_name:cmd.exe” –fields=username,process_id,command_line` (Falcon Query Example)
This command queries the CrowdStrike Falcon dataset for all instances of `cmd.exe` being executed, returning the associated username, process ID, and the full command line. This is crucial for hunting for suspicious command-line activity, such as living-off-the-land binary (LOLBin) execution. To use it effectively, first filter on a high-fidelity detection, then use such queries to scope the full extent of the activity by pivoting on related processes, network connections, and file modifications.
2. Windows Event Log Triage for Identity-Based Attacks
With CrowdStrike’s emphasis on identity-centric security, correlating endpoint data with Windows Event logs is key.
`Get-WinEvent -LogName Security -FilterXPath “[System[EventID=4624]]” | Where-Object { $_.Properties
.Value -like "S-1-5-21-" } | Select-Object -First 20` (PowerShell)
This PowerShell command extracts successful logon events (Event ID 4624) from the Security event log, filtering for events with a specific user SID pattern. This helps identify successful authentication events which can be correlated with Falcon detections to confirm credential access or lateral movement. Execute this in PowerShell on a potentially compromised system to build a timeline of user activity following an initial alert.
<h2 style="color: yellow;">3. Linux Process Lineage Analysis for Threat Hunting</h2>
Comprehensive visibility requires understanding process parent-child relationships on Linux endpoints.
`ps auxeww --forest | grep -E "(nginx|apache|java|python)" | head -n 30` (Bash)
This `ps` command shows a forest view of running processes, filtered for common web server or application runtime processes (like nginx, Apache, Java, Python). The `eww` flags provide extended and wide output for full command-line visibility. This is essential for identifying malicious code injected into or spawned from legitimate web processes, a common technique for persistence and execution. Run this command on critical servers to baseline normal behavior and hunt for anomalies.
<h2 style="color: yellow;">4. API Security Testing for Cloud Visibility Gaps</h2>
MDR must extend to cloud workloads and their APIs. Testing for misconfigurations is critical.
`curl -H "Authorization: Bearer $TOKEN" -X GET https://api.cloudprovider.com/v1/instances | jq '.instances[] | select(.firewall_rules[].port == "22" and .firewall_rules[].cidr == "0.0.0.0/0")'` (Bash)
This curl command uses a cloud provider's API to list all compute instances and then pipes the output to `jq` to parse the JSON and filter for a critical misconfiguration: instances with SSH port 22 open to the world (<code>0.0.0.0/0</code>). Automated AI playbooks can continuously execute such checks. Use this to audit your cloud environment for glaring security gaps that could undermine endpoint protections.
<ol>
<li>KQL Query for Microsoft Sentinel & Defender Integration
Integrating MDR findings with a SIEM like Microsoft Sentinel enables broader correlation.
`SecurityAlert | where ProviderName contains "CrowdStrike" | extend ExtendedProps = parse_json(ExtendedProperties) | extend Technique = ExtendedProps["Technique"] | where Technique == "Credential Dumping"` (Kusto Query Language - KQL)</li>
</ol>
This KQL query for Microsoft Sentinel retrieves security alerts from CrowdStrike and parses the extended JSON properties to filter specifically for alerts related to the "Credential Dumping" technique (T1003). This allows SOC analysts to quickly find all instances of this critical technique across their estate for prioritized investigation. Run this in your Sentinel Logs query window.
<h2 style="color: yellow;">6. YARA Rule for Custom Malware Detection</h2>
AI playbook generation can include deploying custom detections based on discovered IOCs.
[bash]
rule CrowdStrike_MDR_Hunt_Generic_Malware {
meta:
author = "Security Team"
description = "Hunt for potential malware based on common traits"
strings:
$a = {6A 40 58 6A 00 6A 05 51 6A 01 50 E8} // Common shellcode opcodes
$b = /http:\/\/[0-9]{1,3}.[0-9]{1,3}.[0-9]{1,3}.[0-9]{1,3}\// // IP in HTTP string
$c = "MZ" ascii wide
condition:
$c at 0 and (2 of ($a, $b))
}
This YARA rule is designed to hunt for generic malware. It looks for the “MZ” PE header signature combined with either common shellcode byte sequences or HTTP calls made to raw IP addresses. CrowdStrike’s AI can generate and deploy such rules at scale based on attack patterns it observes. Use the YARA command-line tool to scan disk images or memory dumps: yara -r rule.yara /path/to/scan.
7. Mitigating Lateral Movement with Network Segmentation
A key finding from MDR engagements is the need to contain lateral movement.
`Get-NetFirewallRule -DisplayName “Block-SMB-Traffic” | Get-NetFirewallAddressFilter | Where-Object {$.RemoteAddress -eq “Any”} | Set-NetFirewallRule -RemoteAddress “10.0.0.0/8, 172.16.0.0/12, 192.168.0.0/16” -PolicyStore PersistentStore` (PowerShell)
This advanced PowerShell command modifies an existing Windows Firewall rule named “Block-SMB-Traffic”. It changes the rule from blocking SMB traffic to “Any” IP address to instead only block traffic destined for the private RFC1918 IP ranges. This is a more nuanced approach that prevents lateral movement via SMB between internal subnets while allowing necessary traffic to and from the public internet. Test this rule in a lab environment before deployment.
What Undercode Say:
- AI is the Force Multiplier, Not a Replacement: The true innovation lies in AI’s ability to augment human analysts, automating tedious data correlation and playbook generation, which allows the “consistently praised” experts to focus on complex investigation and strategic response.
- Identity is the Unavoidable Frontier: The emphasis on identity-centric security is a direct response to the modern attack chain. Endpoint protection is no longer sufficient; MDR providers must have deep visibility into authentication and authorization events across hybrid environments to detect credential-based lateral movement and privilege escalation.
The Forrester report validates a strategic pivot in cybersecurity. It’s no longer about which vendor has the most signatures; it’s about which platform can most effectively leverage AI to synthesize vast telemetry data into actionable intelligence. CrowdStrike’s positioning highlights that the future of MDR is a symbiotic relationship between advanced AI automation and elite human expertise. The AI handles the scale and speed, while the human analyst provides the context, cunning, and strategic oversight necessary to defeat determined adversaries. This model is becoming the baseline requirement for enterprise security programs.
Prediction:
The integration of AI into MDR, as pioneered by leaders like CrowdStrike, will catalyze a wave of automated, personalized offensive campaigns. Threat actors will themselves leverage AI to analyze the public detection capabilities of major MDR providers, dynamically generating novel attack variants designed to bypass specific AI-driven detections. This will create an accelerated AI-versus-AI arms race in the cybersecurity domain, where the speed of adversarial adaptation will be measured in minutes, not months, fundamentally compressing the lifecycle of offensive techniques and demanding even faster, automated response cycles from defenders.
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IT/Security Reporter URL:
Reported By: David Mcaskill – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
