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Microsoft Security Copilot’s Phishing Triage Agent revolutionizes the way security teams handle phishing incidents by autonomously identifying and resolving false positives with over 95% accuracy. Phishing submissions are among the most frequent alerts security teams face daily, and this AI-driven agent streamlines the process using LLM-driven analysis to assess email content, determine legitimacy, and provide clear explanations for its classifications.
Key Features:
- Automated Triage: Reduces manual workload by filtering false positives efficiently.
- Natural Language Explanations: Offers clear reasoning behind classifications.
- Visual Reasoning Process: Helps analysts understand decision-making.
- Continuous Learning: Adapts based on analyst feedback to improve accuracy.
🔗 Blog: Microsoft Security Copilot – Phishing Triage Agent
You Should Know:
1. How to Simulate Phishing Analysis (Linux/Windows Commands)
Security teams can test phishing detection mechanisms using these commands:
Linux (Analyzing Email Headers)
<h1>Extract email headers for analysis</h1> cat phishing_email.eml | grep -E 'From:|To:|Subject:|Received:' <h1>Check for suspicious URLs in an email</h1> grep -oP 'http[s]?://[^\s<>"]+' phishing_email.eml | sort -u <h1>Analyze SPF/DKIM/DMARC records</h1> dig TXT example.com # Check SPF dig TXT _dmarc.example.com # Check DMARC
#### **Windows (PowerShell Email Analysis)**
<h1>Parse email headers</h1>
Get-Content .\phishing_email.eml | Select-String -Pattern "From:|To:|Subject:|Received:"
<h1>Extract URLs from an email</h1>
Select-String -Path .\phishing_email.eml -Pattern 'http[s]?://[^\s<>"]+' -AllMatches | % { $_.Matches } | Select-Object Value
<h1>Check DNS records</h1>
Resolve-DnsName -Type TXT example.com # SPF
Resolve-DnsName -Type TXT _dmarc.example.com # DMARC
### **2. Automating Phishing Detection with Python**
import re
from email.parser import BytesParser
def analyze_phishing_email(email_path):
with open(email_path, 'rb') as f:
email = BytesParser().parse(f)
print(f"From: {email['from']}\nSubject: {email['subject']}")
urls = re.findall(r'http[s]?://[^\s<>"]+', email.get_payload())
print("Suspicious URLs:", urls)
analyze_phishing_email("phishing_email.eml")
### **3. Enhancing Security with Microsoft Defender (Windows)**
<h1>Check phishing detection status in Defender</h1> Get-MpThreatDetection <h1>Force an update and scan</h1> Update-MpSignature Start-MpScan -ScanType Full
## **What Undercode Say:**
Microsoft’s AI-driven Phishing Triage Agent is a game-changer for SOC teams, reducing false positives and improving response times. However, security professionals should still:
– Manually verify AI classifications in critical cases.
– Regularly update threat intelligence feeds.
– Train staff to recognize phishing attempts beyond automated tools.
For deeper analysis, use Linux commands (grep, dig) or PowerShell to dissect emails and verify DNS records. Automation with Python can further streamline detection, while Microsoft Defender provides an additional layer of security.
## **Expected Output:**
- Phishing email analysis results (headers, URLs).
- DNS record verification (SPF/DKIM/DMARC).
- Automated script outputs for bulk email scanning.
- Microsoft Defender threat detection logs.
🔗 Reference: Microsoft Security Copilot – Phishing Triage Agent
References:
Reported By: Markolauren Phishing – Hackers Feeds
Extra Hub: Undercode MoN
Basic Verification: Pass ✅
