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Introduction
In the relentless battle against cyber adversaries, traditional threat hunting often leaves organizations reacting to incidents after they occur. The paradigm shift toward “Glass Raising”—a proactive, visibility-first defense strategy—empowers security teams to anticipate attacks before they manifest. This article dissects how elite teams like RedEntry operationalize this transition, moving from reactive hunting to predictive control using advanced AI-driven analytics, automated containment, and hardened infrastructure.
Learning Objectives
- Implement automated threat intelligence feeds into SIEM/SOAR workflows to reduce mean time to detection (MTTD)
- Execute Linux and Windows commands for memory forensics, log analysis, and persistence mechanism detection
- Deploy cloud hardening techniques and API security gateways to preempt exploitation attempts
You Should Know
- Operationalizing the Transition from Threat Hunting to Glass Raising
The core concept of “Glass Raising” extends beyond visibility—it creates an immutable, real-time pane into adversarial tactics, techniques, and procedures (TTPs). Where threat hunting assumes a compromise has occurred, glass raising erects barriers that force attackers to reveal themselves during reconnaissance.
Step‑by‑step guide to build a Glass Raising pipeline:
- Deploy sensor grids across endpoints, network flows, and cloud audit logs using osquery (Linux/Windows).
Linux command to install osquery:
`sudo apt-get install osquery -y` (Debian/Ubuntu)
Windows (PowerShell as Admin):
`choco install osquery`
- Configure sysmon (Windows) or auditd (Linux) for high-fidelity event collection.
Linux auditd rule to monitor file writes to sensitive directories:
`sudo auditctl -w /etc/passwd -p wa -k passwd_monitor`
Windows sysmon config (install with):
`sysmon64 -accepteula -i sysmon-config.xml`
- Ingest logs into a SIEM (e.g., Wazuh, Splunk) and create correlation rules for low-and-slow scanning.
Example Sigma rule for port scan detection:
title: Port Scan Detection status: experimental logsource: category: firewall detection: selection: dest_port: [22, 3389, 443] count: >10 condition: selection
- Automate response via SOAR (e.g., Shuffle, TheHive) to trigger IP blocking or container quarantine.
Linux iptables drop on detection event:
`sudo iptables -A INPUT -s $MALICIOUS_IP -j DROP`
Windows netsh equivalent:
`netsh advfirewall firewall add rule name=”Block_IP” dir=in action=block remoteip=$MALICIOUS_IP`
5. Validate the pipeline with purple-team exercises using Atomic Red Team:
`Invoke-AtomicTest T1059.001 -GetPrereqs` (PowerShell)
2. AI-Driven Threat Intelligence Enrichment for Proactive Defense
Static IOCs are obsolete; AI models trained on behavioral anomalies predict zero-day patterns. Integrate large language models (LLMs) to parse unstructured threat reports and generate detection rules on the fly.
Step‑by‑step AI enrichment pipeline:
- Set up an open-source ML pipeline using Apache NiFi + TensorFlow.
Pull threat feeds (AlienVault OTX, MISP) via API:
`curl -X GET “https://otx.alienvault.com/api/v1/pulses/subscribed” -H “X-OTX-API-KEY: YOUR_KEY”`
2. Normalize and vectorize observables (IPs, hashes, domains) using Python + scikit-learn.
from sklearn.feature_extraction.text import TfidfVectorizer iocs = ["evil.com", "bad.exe", "192.168.1.1"] vec = TfidfVectorizer().fit_transform(iocs)
- Deploy a real-time classifier (Random Forest) to score incoming logs.
Command to run inference on a syslog stream:
`tail -f /var/log/syslog | python3 score_logs.py`
- Create automated playbooks in TheHive that escalate high-risk scores to human analysts.
TheHive API alert creation:
`curl -u API_KEY: -H “Content-Type: application/json” -X POST https://thehive/api/v1/alert -d ‘{“title”:”High Risk Score”,”type”:”external”}’`
5. Monitor drift with Prometheus and retrain models weekly using new incident data.
PromQL query to check anomaly detection false positive rate:
`rate(anomaly_detection_fp_total
)`</h2>
<ol>
<li>Hardening Attack Surfaces on Cloud and API Gateways</li>
</ol>
Glass raising demands that every entry point—from container registries to GraphQL endpoints—enforces zero-trust principals and immutable infrastructure.
<h2 style="color: yellow;">Step‑by‑step cloud/API hardening:</h2>
<ol>
<li>Enforce OAuth2 with PKCE for all public APIs; block API keys that lack scope restrictions.
AWS WAF rule to block requests missing Authorization header:
[bash]
{
"Name": "BlockNoAuth",
"Priority": 0,
"Action": { "Block": {} },
"VisibilityConfig": { "SampledRequestsEnabled": true },
"Statement": { "ByteMatchStatement": { "SearchString": "Bearer", "FieldToMatch": { "Headers": { "Name": "Authorization" } } } }
}
Scan container images pre-deployment using Trivy or Grype.
Linux command to scan an image:
`trivy image python:3.9-slim –severity HIGH,CRITICAL`
Fail CI/CD if critical findings exist:
`if [ $(trivy image –exit-code 0 –severity CRITICAL python:3.9-slim | grep -c “CRITICAL”) -gt 0 ]; then exit 1; fi`
3. Harden Kubernetes RBAC by disabling default service account tokens.
`kubectl patch serviceaccount default -p ‘{“automountServiceAccountToken”: false}’`
- Implement mTLS between microservices using Istio or Linkerd.
Check mTLS status in Istio:
`kubectl exec -it $(kubectl get pod -l app=sleep -o jsonpath='{.items
.metadata.name}') -c sleep -- curl -s https://httpbin:8000/headers | grep "X-Forwarded-Client-Cert"`
5. Deploy a Web Application Firewall (WAF) rule to block SQLi and XSS using ModSecurity CRS.
<h2 style="color: yellow;">OWASP CRS paranoia level 2 configuration:</h2>
<h2 style="color: yellow;">`SecAction "id:900000,phase:1,setvar:tx.paranoia_level=2"`</h2>
<h2 style="color: yellow;">4. Memory Forensics and Persistence Eviction (Linux/Windows)</h2>
Attackers rely on fileless malware and rootkits. Glass raising includes scheduled memory scans and autorun audits to break persistence.
<h2 style="color: yellow;">Step‑by‑step memory and persistence sweep:</h2>
<h2 style="color: yellow;">1. Acquire memory dump on Linux using LiME.</h2>
<h2 style="color: yellow;">`sudo insmod lime.ko "path=/tmp/mem.dump format=lime"`</h2>
<h2 style="color: yellow;">Windows using winpmem:</h2>
<h2 style="color: yellow;">`winpmem_2.1.exe -o memdump.raw`</h2>
<ol>
<li>Analyze with Volatility 3 to detect hidden processes. </li>
</ol>
<h2 style="color: yellow;">`vol3 -f memdump.raw windows.pslist.PsList`</h2>
Look for processes with mismatched EPROCESS and linked-list entries.
<ol>
<li>Enumerate scheduled tasks (Windows) and cron jobs (Linux) for anomalies. </li>
</ol>
<h2 style="color: yellow;">Windows (PowerShell):</h2>
`Get-ScheduledTask | Where-Object {$_.State -ne "Disabled"} | Format-Table TaskName, TaskPath`
<h2 style="color: yellow;">Linux:</h2>
<h2 style="color: yellow;">`crontab -l; cat /etc/crontab; ls -la /etc/cron.`</h2>
<h2 style="color: yellow;">4. Check LD_PRELOAD and DLL injection vectors.</h2>
<h2 style="color: yellow;">Linux:</h2>
<h2 style="color: yellow;">`grep -H "" /proc//environ 2>/dev/null | grep LD_PRELOAD`</h2>
<h2 style="color: yellow;">Windows (autoruns from Sysinternals):</h2>
<h2 style="color: yellow;">`autorunsc64 -a -c -h -s > autoruns.csv`</h2>
<ol>
<li>Harden against persistence by setting SELinux booleans (Linux) or Windows Defender ASR rules. </li>
</ol>
<h2 style="color: yellow;">SELinux to prevent module loading:</h2>
<h2 style="color: yellow;">`setsebool -P domain_kernel_load_modules 0`</h2>
<h2 style="color: yellow;">ASR rule to block Office script injection:</h2>
<h2 style="color: yellow;">`Add-MpPreference -AttackSurfaceReductionRules_Ids "92E97FA1-2EDF-4476-BDD6-9DD0B4DDDC7B" -AttackSurfaceReductionRules_Actions Enabled`</h2>
<ol>
<li>Vulnerability Exploitation and Mitigation Exercises for SOC Teams</li>
</ol>
To raise the glass, defenders must think like attackers. Perform controlled exploitation of common vectors (SSRF, log4j, SMB relay) then deploy mitigations.
<h2 style="color: yellow;">Step‑by‑step exercise (ethical lab only):</h2>
<ol>
<li>Spin up a vulnerable container (Metasploitable 3) on an isolated network. </li>
</ol>
<h2 style="color: yellow;">Docker command:</h2>
`docker run -it --rm --name vulnerable --network=isolated -p 8080:80 vulnerables/web-dvwa`
<h2 style="color: yellow;">2. Exploit SSRF via internal metadata endpoint.</h2>
<h2 style="color: yellow;">Payload:</h2>
`http://169.254.169.254/latest/meta-data/`
Detection: monitor egress requests to RFC1918 IPs from web tier.
<h2 style="color: yellow;">3. Mitigate by implementing egress filtering and IMDSv2.</h2>
<h2 style="color: yellow;">AWS CLI to enforce IMDSv2:</h2>
`aws ec2 modify-instance-metadata-options --instance-id i-123456 --http-tokens required --http-endpoint enabled`
<ol>
<li>Test log4j JNDI injection using a benign payload. </li>
</ol>
<h2 style="color: yellow;">Command to check for vulnerable libs:</h2>
<h2 style="color: yellow;">`find / -name "log4j-core-.jar" 2>/dev/null`</h2>
<h2 style="color: yellow;">Mitigation: set `LOG4J_FORMAT_MSG_NO_LOOKUPS=true`</h2>
<h2 style="color: yellow;">`export LOG4J_FORMAT_MSG_NO_LOOKUPS=true`</h2>
<ol>
<li>Run SMB relay against Windows with Responder, then disable NTLMv1 and enforce SMB signing. </li>
</ol>
<h2 style="color: yellow;">Disable NTLMv1 via GPO:</h2>
<h2 style="color: yellow;">`Set-ItemProperty -Path "HKLM:\SYSTEM\CurrentControlSet\Control\Lsa" -Name "LmCompatibilityLevel" -Value 5`</h2>
<h2 style="color: yellow;">Enforce SMB signing:</h2>
<h2 style="color: yellow;">`Set-SmbServerConfiguration -RequireSecuritySignature $true -Force`</h2>
<ol>
<li>Building a Glass Raising Dashboard with Open Source Tools</li>
</ol>
Visualize the shift from reactive alerts to predictive posture using Grafana and Elasticsearch.
<h2 style="color: yellow;">Step‑by‑step dashboard construction:</h2>
<ol>
<li>Install ELK stack (Elasticsearch, Logstash, Kibana) on a dedicated monitoring host. </li>
</ol>
<h2 style="color: yellow;">`sudo apt-get install elasticsearch logstash kibana -y` (Linux)</h2>
<h2 style="color: yellow;">Windows via Chocolatey:</h2>
<h2 style="color: yellow;">`choco install elasticsearch logstash kibana`</h2>
<ol>
<li>Configure Logstash to consume syslog, Windows Event Logs, and cloud audit trails. </li>
</ol>
<h2 style="color: yellow;">Sample Logstash input for Windows:</h2>
[bash]
input { winlogbeat { port => 5044 } }
filter { grok { match => { "message" => "%{TIMESTAMP_ISO8601:timestamp} %{LOGLEVEL:level} %{GREEDYDATA:details}" } } }
output { elasticsearch { hosts => ["localhost:9200"] } }
- Create Kibana visualizations for “TTPs blocked” vs “hunting leads” over time.
Aggregation query for Top 10 MITRE ATT&CK techniques:
`GET /logs-/_search { “aggs”: { “techniques”: { “terms”: { “field”: “mitre_technique.keyword”, “size”: 10 } } } }`
4. Set up Grafana to pull data from Elasticsearch and alert on thresholds (e.g., >5 failed logins per minute).
Grafana alert rule (JSON):
{
"condition": "A > 5",
"query": "SELECT COUNT() FROM \"winlogbeat\" WHERE event_id=4625 GROUP BY time(1m)",
"noDataState": "no_data"
}
- Embed live glass panel on SOC wall using Raspberry Pi + Chromium in kiosk mode.
`chromium-browser –kiosk http://grafana:3000/d/glass`
What Undercode Say
- Shift left from hunting to prevention: Glass Raising eliminates the dwell time advantage adversaries rely on. Every command listed above forces attackers to burn zero-days early.
- AI is not magic, it’s math+context: The AI pipeline works only when fed clean, normalized telemetry. Pair ML predictions with human validation—false positives still kill SOC credibility.
- Persistence is the attacker’s last resort: If you automate memory sweeps and autorun audits weekly, even fileless malware becomes transient noise rather than a backdoor.
The transition from threat hunting to glass raising isn’t a product purchase—it’s a cultural and technical engineering overhaul. Teams at RedEntry demonstrate that by weaving real-time visibility, AI enrichment, and hardened APIs into a single pane of glass, chaos transforms into controlled, actionable intelligence. The industry will move from “What did they break?” to “What did we preempt?” as the primary SOC metric.
Prediction
By 2027, regulatory frameworks (e.g., revised NIS2, SEC rules) will mandate “continuous adversarial emulation” as a compliance requirement, effectively making Glass Raising a legal baseline. Organizations failing to deploy automated, predictive defenses will face breach liability multipliers. The convergence of LLM-based TTP prediction and infrastructure-as-code hardening will commoditize threat hunting, forcing MSSPs to pivot toward outcome-based “prevention SLAs” rather than traditional MDR. Expect open-source Glass Raising dashboards (like the Grafana example) to replace legacy SIEM frontends within 18 months.
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Reported By: Omri Zachay – Hackers Feeds
Extra Hub: Undercode MoN
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