Best Data Security Software for Python
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Compare the Top Data Security Software that integrates with Python as of July 2025
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This a list of Data Security software that integrates with Python. Use the filters on the left to add additional filters for products that have integrations with Python. View the products that work with Python in the table below.
What is Data Security Software for Python?
Data security software is designed to protect sensitive data from unauthorized access, theft, or corruption. It includes a variety of tools and features such as encryption, access control, data masking, and backup and recovery to ensure that data remains secure at rest, in transit, and during processing. These solutions help organizations comply with data protection regulations, such as GDPR and HIPAA, by safeguarding personal, financial, and business data. Data security software often integrates with other IT security systems to provide comprehensive protection against cyberattacks, insider threats, and data breaches, ensuring that critical information remains protected. Compare and read user reviews of the best Data Security software for Python currently available using the table below. This list is updated regularly.
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Tenzir
Tenzir
Tenzir is a data pipeline engine specifically designed for security teams, facilitating the collection, transformation, enrichment, and routing of security data throughout its lifecycle. It enables users to seamlessly gather data from various sources, parse unstructured data into structured formats, and transform it as needed. It optimizes data volume, reduces costs, and supports mapping to standardized schemas like OCSF, ASIM, and ECS. Tenzir ensures compliance through data anonymization features and enriches data by adding context from threats, assets, and vulnerabilities. It supports real-time detection and stores data efficiently in Parquet format within object storage systems. Users can rapidly search and materialize necessary data and reactivate at-rest data back into motion. Tension is built for flexibility, allowing deployment as code and integration into existing workflows, ultimately aiming to reduce SIEM costs and provide full control. -
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Roseman Labs
Roseman Labs
Roseman Labs enables you to encrypt, link, and analyze multiple data sets while safeguarding the privacy and commercial sensitivity of the actual data. This allows you to combine data sets from several parties, analyze them, and get the insights you need to optimize your processes. Tap into the unused potential of your data. With Roseman Labs, you have the power of cryptography at your fingertips through the simplicity of Python. Encrypting sensitive data allows you to analyze it while safeguarding privacy, protecting commercial sensitivity, and adhering to GDPR regulations. Generate insights from personal or commercially sensitive information, with enhanced GDPR compliance. Ensure data privacy with state-of-the-art encryption. Roseman Labs allows you to link data sets from several parties. By analyzing the combined data, you'll be able to discover which records appear in several data sets, allowing for new patterns to emerge. -
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Noma
Noma
From development to production and from classic data engineering to AI. Secure the development environments, pipelines, tools, and open source components that make up your data and AI supply chain. Continuously discover, prevent, and fix AI security and compliance risks before they make their way to production. Monitor your AI applications in runtime, detect and block adversarial AI attacks, and enforce app-specific guardrails. Noma seamlessly embeds across your data and AI supply chain and AI applications, mapping all your data pipelines, notebooks, MLOps tools, open-source AI components, first- and third-party models, and datasets, automatically generating a comprehensive AI/ML-BOM. Noma continuously identifies and provides actionable remediations for security risks such as misconfigurations, AI vulnerabilities, and against-policy training data usage throughout your data and AI supply chain, enabling you to proactively improve your AI security posture.
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