Whether or not you’ve used Python, you’ve surely heard of it thanks to its popularity across many disciplines that rely on computing to model, analyze, automate, and visualize data. Python is not just for software development. It is also used in AI and machine learning, data science, robotics, embedded systems, video game development, and scientific computing.
Yearly developer surveys, such as those from Stack Overflow and the TIOBE Index, confirm again and again that Python is not only one of the most widely-used, it is the most desired language—the one that developers want to learn and use in the next year.
But what about using Python in cybersecurity? In this blog, you’ll learn about eight cybersecurity use cases for Python and the popular packages that address common security problems.
Security practitioners use Python for cybersecurity because it is versatile and easy to learn. There’s also a vibrant community that maintains many open-source software (OSS) libraries that serve many cybersecurity needs. These three reasons are precisely why Coursera placed scripting with Python as the #1 skill to learn for security analysts, but let’s review this in detail.
Python is considered easy to learn thanks to its simple syntax that resembles natural language. This puts Python squarely in the category of high-level languages that abstract away low-level details, which allows beginners and seasoned coders alike to focus on their logic instead of technical complexities. Python’s architecture further simplifies development:
Python’s versatility comes from its robust standard library, its ability to integrate with other tools, and the active community that backs it with OSS. The Python standard library is known to be particularly robust, with modules for a wide variety of tasks that can be components for a variety of cybersecurity use cases. This includes file input/output, data manipulation, web development, networking, mathematics, scientific computing, cryptography, data serialization, machine learning, and much more.
With its extensive resources, Python is ideal for rapid prototyping and iteration. This enables quick development cycles for security tools and solutions, which can be particularly helpful for adapting to evolving attacker techniques.
Python’s resources paired with its scripting capabilities also make it popular for automating routine or menial tasks, further cementing its role as the top choice to streamline cybersecurity operations.
Next, let’s get into the key use cases for Python in cybersecurity, starting with pen testing.
With the goal of simulating cyberattacks, penetration testing requires a variety of tooling to break into systems, and Python delivers.
Python has a vast collection of libraries that are specifically designed for penetration testing, security analysis, and networking tasks. The benefit of working with these libraries is speeding up the development process by using ready-made functionality. Some of the popular libraries include:
One of Python’s greatest strengths is its ability to automate repetitive tasks, which is essential in penetration testing. Tasks like vulnerability scanning, brute forcing, and report generation can be easily automated using Python scripts, saving time and reducing the risk of human error during testing.
Python also integrates well with other penetration testing tools and frameworks, such as Metasploit, Nmap, and Burp Suite. Python scripts can be used to extend existing functionality to launch attacks, parse results, and combine data from different tools for comprehensive analysis.
As a discipline, detection engineering focuses on identifying malicious activity and security threats by creating rules to detect suspicious patterns in various systems and environments. It involves analyzing vast datasets, building detection algorithms, and refining rules to ensure they effectively identify potential attacks.
Detection engineering commonly occurs within threat detection platforms, like a SIEM. Because Python excels at data analysis, most platforms offer SDKs, extensions, or APIs to interact with the platform using Python. For example, you might update your SIEM configurations or run a scheduled search via an API endpoint using a Python script.
Some platforms, like Panther, use Python to write detection logic itself, taking advantage of its versatility and robust library support. The open-source project Sigma codifies detection rules in a platform-agnostic standard and uses Python-based tools like pySigma and sigma-cli to convert the rules into platform-specific queries. Writing detection rules in code, known as detection-as-code (DaC), allows engineers to create highly customized logic, manage detection workflows with version control, and integrate with CI/CD pipelines for streamlined development—all for high-signal security alerts.
Finally, Python’s scripting capabilities make it easy to automate repetitive tasks like scanning logs, querying large datasets, and correlating threat intelligence feeds with internal data to identify anomalies. This is why many platforms provide Python-based integrations or offer APIs for automation and connecting with other security tools.
Digital Forensics and Incident Response (DFIR) aims to gather, analyze, and preserve digital evidence to understand how a breach occurred and to respond swiftly to threats. It covers a broad range of tasks such as memory forensics, disk analysis, malware examination, network traffic monitoring, and log analysis, not to mention reporting.
Python libraries provide ready-made tools to automate and simplify these processes, enabling the analysis of large datasets, recovery of lost data, malware detection and remediation, and timeline reconstruction. In fact, you can design a top-to-bottom incident response workflow using only Python libraries.
Here are a few Python libraries that could be used within DFIR:
Application security focuses on protecting software applications from vulnerabilities, threats, and attacks, aiming to secure their data, functionality, and user interactions against potential exploits.
In static analysis for application security, Python scripts can help review code to find insecure patterns, scan dependencies, and check for common vulnerabilities like SQL injection or cross-site scripting (XSS). For dynamic analysis, Python is often used to pen test live applications, checking how they handle malicious inputs, manage sessions, and respond to security events.
Popular libraries used in application security include Requests and SQLMap, as well as:
Malware analysis focuses on uncovering what malware does, how it works, and the damage it can cause, often using reverse engineering techniques to understand its internal logic and vulnerabilities.
In static malware analysis, Python scripts can be used to disassemble malware samples, extract strings, analyze file headers, and identify suspicious sections within the code. For dynamic analysis, Python can automate analysis techniques, such as sandbox testing to observe malware behavior, including API calls, file system changes, and network activity.
Sometimes malware analysts and reverse engineers need custom tools to analyze specific malware samples or bypass particular anti-analysis techniques. Naturally, Python’s versatility and extensive library support enable analysts to quickly build custom tools to adapt to evolving malware techniques.
Python’s scripting capabilities make it ideal for automating repetitive tasks like extracting information from files, scanning large datasets, or monitoring network traffic. Popular libraries used in malware analysis include Scapy and Cryptography, as well as:
And of course, Python integrates with numerous cybersecurity tools and frameworks used for malware analysis, such as
Network security involves protecting computer networks from unauthorized access, misuse, and attacks, with a focus on detecting and preventing network-based attacks. As you might expect, Python has many libraries that perform network security functions.
In network traffic analysis, a tool like Scapy helps dissect packets, inspect data layers, and identify anomalies. For intrusion detection, Python scripts can analyze logs, filter network traffic, and identify patterns of malicious behavior that indicate potential attacks.
Like most cybersecurity work, network security involves repetitive tasks like monitoring traffic, scanning for vulnerabilities, and enforcing security policies. Python’s scripting capabilities make automating these tasks easy and efficient. For example, the Python library NAPALM automates interactions with networking devices, ensuring that configurations are enforced and applied consistently.
Other popular Python libraries for network security include:
Python also integrates well with many network security tools and frameworks, like extending the NIDS capabilities of Snort using Python scripts, and creating custom Python scripts with Zeek to log network activity and detect threats.
Data science analyzes, models, and interprets data to extract insights in order to make data-informed decisions. Statistical analysis, data visualization, big data analytics, and machine learning (ML) are just a few methods used across the many industries that rely on data science to identify patterns and make predictions.
Machine learning is everywhere within cybersecurity, and still proving its value. Machine learning aims to predict threats and automate incident response by using algorithms to detect anomalies, vulnerabilities, and malicious activities that traditional methods might overlook.
Otherwise, you’ll find data science tools and techniques used in threat hunting. Threat hunting is the proactive search for threats or malicious activity within an organization’s systems by identifying patterns, anomalies, and suspicious behaviors through in-depth analysis of security data.
There are numerous, powerful data handling libraries that are either natively designed to be used with Python or have a comprehensive and easy-to-use Python API for development. To name just a few, there’s Scikit-Learn, TensorFlow, PyTorch, and Keras, all of which simplify the development of ML models that can identify and classify cybersecurity threats with high accuracy.
Then there are Python libraries like Pandas and NumPy, which provide essential tools for manipulating large datasets, cleaning data, and exploratory data analysis (EDA), a critical step in preparing data for machine learning models. In threat hunting, these Python libraries are commonly used in conjunction with Jupyter Notebooks, an open-source, web-based platform that allows users to create and share documents containing live code, visualizations, equations, and narrative text to explain the methodology and steps taken to perform specific tasks.
For threat hunting, Jupyter Notebooks allow teams to analyze security events using Python while also documenting, standardizing, and sharing detection playbooks, which otherwise is a pain point. As a starting point, read Rodriguez’s series Threat Hunting with Jupyter Notebooks, then check out the Threat Hunter’s Playbook, an open-source project for sharing detection logic, adversary tradecraft, and resources.
You might be expecting it at this point, but Python has many libraries that simplify cryptographic operations. Popular libraries like PyCryptodome, Cryptography, hashlib, and SageMath’s cryptography modules provide implementations of various cryptographic algorithms, making it easy to handle encryption, decryption, hashing, and key management.
Moreover, these libraries are well-documented, regularly updated, and cover a wide range of cryptographic standards like AES, RSA, SHA, and more. Python also supports modern cryptographic techniques, including elliptic curve cryptography (ECC), quantum-resistant algorithms, and zero-knowledge proofs.
What else? There’s a host of Python bindings, wrappers, and integrations that enable you to use popular tools with Python, like Hashcat, the password and hash cracking tool, and OpenSSL, for testing SSL/TLS vulnerabilities and key management.
Did you catch the theme? Python is versatile, easy to learn, and promotes rapid development and prototyping. Python also integrates well with a wide range of tools and is backed by a rich ecosystem of tools and community support. Above all, Python is excellent for general purpose scripting and automation, which is hot on every security engineers list!
With so many uses, you truly can’t go wrong learning Python. Most often you’ll find Python used in research and development, pen testing, malware analysis, machine learning, and cryptography. Ultimately, learning Python is a personal choice, as there are many programming languages to choose from, and security practitioners typically need to be multilingual.
If you’re ready to jump in, there’s a wealth of free online resources to learn Python. If you prefer books from established publishers, check out No Starch Press, which has books on using Python for cybersecurity specifically.
Threat detection benefits from Python’s versatility and ease-of-use, which is exactly why Panther fashioned its detection-as-code (DaC) practice around Python. Learn how Panther is shaping the future of detection-as-code with pypanther, a Python library for scaling SIEM rule management and generating the most contextualized alerts for your organization.
Panther is the leading cloud-native SIEM offering detection-as-code for highly-contextualized, high-signal alerts, and backed by a serverless security data lake that provides 365 days of hot storage for rapid investigations. Request a demo to learn how Panther can solve your threat detection use cases.
