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Boost SOC efficiency with Python security automation
Resource-strapped SOCs need better incident response, threat hunting and report generation. Explore how automating tasks with Python makes life easier for security teams.
Security operations center teams are looking for an edge amid budget constraints, staff shortages, high alert volumes, tool sprawl, fragmented data and sophisticated cyberattacks that require faster responses than ever. Automation offers that edge. More and more SOC teams are automating defenses and incident response to secure data, ensure availability and maintain regulatory compliance.
One relatively easy-to-use and inexpensive tool that helps achieve this is Python. Let's examine how to use Python to automate common security tasks and the innumerable benefits it provides SOC teams.
Automation with Python
The simplicity and cross-platform capabilities of Python automation make it an excellent choice in cybersecurity scenarios. Consider the following use cases:
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Log parsing and correlation. Python can retrieve and process logs from firewalls, routers, intrusion detection and prevention systems, EDR tools and other systems to identify suspicious activity, such as failed login attempts or system reboots.
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Report generation. Use Python to generate daily, weekly and monthly incident reports to establish timelines and identify incident response bottlenecks.
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Incident response automation. Python scripts can isolate hosts, configure routers and firewalls to block IPs or domains, and manage incident ticketing systems such as Jira.
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Threat hunting. Schedule Python scripts to hunt for threats in SIEM or EDR data and identify anomalous activity.
Why Python is a good choice for cybersecurity automation
Python continues to enjoy widespread popularity among developers, but it's also a useful tool for administrators, cybersecurity experts and other technical users. Three of Python's primary benefits are its extensive library offerings, its extreme cross-platform support and its ease of use.
Python draws on extensive libraries
Python libraries are reusable code collections to extend the language's native functionality. They prevent users from needing to write every aspect of the code themselves. Python includes a standard library, but many additional, purpose-specific libraries exist in the Python ecosystem.
The following libraries and related Python utilities greatly extend the language's security automation capabilities, allowing integration into many different security scenarios:
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requests: Use to connect to APIs made available by SIEM and security orchestration, automation and response (SOAR) tools, scanners or endpoint monitoring software.
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pandas: Use for data analysis and log file parsing, providing structured text suitable for filtering and event correlation.
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re: Use for pattern matching and parsing unstructured text, logs and email.
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scapy: Use for custom network scans, protocol fuzzing and traffic analysis using packet inspection.
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paramiko: Use to automate SSH tasks, including command execution, log file collection, agent deployments and configuration remediation.
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python-nmap: This Nmap wrapper enables Python-based scanning and results parsing, useful for asset inventories and vulnerability checking.
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Elasticsearch and opensearch-py: These clients automate log file searches, detection queries and threat enrichment in Elasticsearch and OpenSearch SOC environments.
Major cloud platforms also offer SDKs to interact with essential applications and services. Manage these libraries using the Python pip package manager. package manager.
Python integrates with everything
Python uses the same interpreter and standard library across Linux, macOS and Windows platforms. Cloud service providers offer Python on Linux or Windows platforms to ensure the same experience as non-cloud deployments.
Python runs on or interacts with standard components, applications and APIs, including Windows, Linux, macOS and many Unix-like network device OSes; containers; REST APIs; databases; cloud environments; and common SIEM, SOAR and EDR tools. Compatibility between OSes, local installations and cloud deployments is a matter of configuration differences rather than code changes.
Deploy Python using a system's native package manager or installer. Plan to standardize the installation, including version control, then roll out the Python deployment using Ansible or custom scripts. The deployment will vary depending on whether it is targeting an endpoint requiring the basic Python interpreter or a developer or administrator system used to author and test Python code.
Python is easy to learn
Python is a remarkably easy language with a community that offers many learning resources. It's suitable for nondevelopers, security professionals, and systems and network administrators.
The official Python for Beginners guide is an ideal place to start, and there are plenty of online courses, tutorials and resources available.
Python enables rapid prototyping and configuration changes. Python apps are not compiled, making them easier to use, and deployments to remote systems are straightforward. Python is open source, and many of the top integrated development environments for it are free. Examples include PyCharm, Visual Studio Code and Thonny. Users can also use standard code editors, such as Vim.
Real-world Python example: Log scanner
Thousands of potential Python automation tasks exist for cybersecurity use. To better understand Python's simplicity and capability, review this sample script that scans log files for keywords.
import time
LOGFILE = "system.log"
KEYWORDS = ["error", "failed", "unauthorized", "denied"]
def monitor_logs():
print("Reviewing log entries...")
with open(LOGFILE, "r") as f:
f.seek(0, 2) # go to end of file
while True:
line = f.readline()
if not line:
time.sleep(0.5)
continue
for keyword in KEYWORDS:
if keyword.lower() in line.lower():
print(f"[ALERT] Suspicious entry: {line.strip()}")
# monitor_logs()
The script notifies users of the keywords error, failed, unauthorized and denied so they can take further action. Python makes log file monitoring simpler with script scheduling on multiple web servers and notifications sent to workstations. Scripts can be combined with additional automation processes to enhance usefulness.
Online Python tools
Users don't have to create Python tools from scratch. The following existing projects provide curated, effective security apps:
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python-pentest-tools is an extensive list of standard Python security libraries, resources and tools.
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python-for-cybersecurity is an older list of Python scripts associated with the Infosec Institute's Python for Cybersecurity learning resources.
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security-scripts is a series of Python and Bash scripts related to security auditing.
Many more exist. Be sure to run these scripts in test environments before deploying them to production systems.
Python's interoperability, extensive library catalog and ease of learning make it an excellent choice for addressing and minimizing the internal and external challenges faced by SOCs.
Damon Garn owns Cogspinner Coaction and provides freelance IT writing and editing services. He has written multiple CompTIA study guides, including the Linux+, Cloud Essentials+ and Server+ guides, and contributes extensively to Informa TechTarget, The New Stack and CompTIA Blogs.
