Best AI Security Software for Python
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Compare the Top AI Security Software that integrates with Python as of April 2026
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This a list of AI 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 AI Security Software for Python?
AI security software is a technology that uses artificial intelligence (AI) to protect online systems from malicious attacks. AI security software can also ensure that companies are using AI software and generative AI tools safely. It can detect potential threats and blocks them before they cause damage. AI security software provides additional protection beyond traditional methods such as firewalls, antivirus, and intrusion detection systems. AI security software can be used to protect not only corporate networks but also individual computers from cyberattacks. The AI algorithms use machine learning techniques to learn about the changing patterns of malicious behavior in order to identify new threats more quickly and accurately. It also has the ability to adapt its responses over time, making it a powerful tool for combating ever-evolving cyber threats. Many companies now deploy AI security software as part of their comprehensive cybersecurity strategy. Compare and read user reviews of the best AI Security software for Python currently available using the table below. This list is updated regularly.
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1
ZeroPath
ZeroPath
ZeroPath (YC S24) is an AI-native application security platform that delivers comprehensive code protection beyond traditional SAST. Founded by security engineers from Tesla and Google, ZeroPath combines large language models with advanced program analysis to find and automatically fix vulnerabilities. ZeroPath provides complete security coverage: 1. AI-powered SAST for business logic flaws & broken authentication 2. SCA with reachability analysis 3. Secrets detection and validation 4. Infrastructure as Code 5. Automated patch generation. any more... ZeroPath delivers 2x more real vulnerabilities with 75% fewer false positives. Our research team has been successful in finding vulns like critical account takeover in better-auth (CVE-2025-61928, 300k+ weekly downloads), identifying 170+ verified bugs in curl, and discovering 0-days in production systems at Netflix, Hulu, and Salesforce. Trusted by 750+ companies and performing 200k+ code scans monthly.Starting Price: Free -
2
Criminal IP
AI SPERA
Criminal IP equips security teams with the actionable Threat Intelligence needed to proactively identify, analyze, and respond to emerging threats. Powered by AI and OSINT, it delivers threat scoring, reputation data, and real-time detection of a wide array of malicious indicators, ranging from C2 servers and IOCs to masking services like VPNs, proxies, and anonymous VPNs, across IPs, domains, and URLs. Its API-first architecture ensures seamless integration into security workflows to boost visibility, automation, and response.Starting Price: $0/month -
3
Backslash Security
Backslash
Ensure the security of your code and open sources. Identify externally reachable data flows and vulnerabilities for effective risk mitigation. By identifying genuine attack paths to reachable code, we enable you to fix only the code and open-source software that is truly in use and reachable. Avoid unnecessary overloading of development teams with irrelevant vulnerabilities. Prioritize risk mitigation efforts more effectively, ensuring a focused and efficient security approach. Reduce the noise CSPM, CNAPP, and other runtime tools create by removing unreachable packages before running your applications. Meticulously analyze your software components and dependencies, identifying any known vulnerabilities or outdated libraries that could pose a threat. Backslash analyzes both direct and transitive packages, ensuring 100% reachability coverage. It outperforms existing tools that solely focus on direct packages, accounting for only 11% of packages. -
4
LLM Guard
LLM Guard
By offering sanitization, detection of harmful language, prevention of data leakage, and resistance against prompt injection attacks, LLM Guard ensures that your interactions with LLMs remain safe and secure. LLM Guard is designed for easy integration and deployment in production environments. While it's ready to use out-of-the-box, please be informed that we're constantly improving and updating the repository. Base functionality requires a limited number of libraries, as you explore more advanced features, necessary libraries will be automatically installed. We are committed to a transparent development process and highly appreciate any contributions. Whether you are helping us fix bugs, propose new features, improve our documentation, or spread the word, we would love to have you as part of our community.Starting Price: Free -
5
Golf
Golf
GolfMCP is an open source framework designed to streamline the creation and deployment of production-ready Model Context Protocol (MCP) servers, enabling organizations to build secure, scalable AI-agent infrastructure without worrying about boilerplate. It allows developers to define tools, prompts, and resources as simple Python files, after which Golf handles routing, authentication, telemetry, and observability, so you focus on logic, not plumbing. The platform supports enterprise authentication (JWT, OAuth Server, API key), automatic telemetry, and a file-based structure that eliminates decorators or manual schema wiring. With built-in utilities for LLM interactions, error logging, OpenTelemetry integration, and deployment tools (such as a CLI with golf init, golf build dev, golf run), Golf provides a full stack for agent-native services. Included also is the Golf Firewall, an enterprise-grade security layer for MCP servers that enforces token validation.Starting Price: Free -
6
DryRun Security
DryRun Security
DryRun Security brings AI Native SAST and Agentic Code Security to your code, so application security and dev teams can stop triaging noise and start fixing real risk. Our Contextual Security Analysis (CSA) engine reasons about code intent, exploitability, and impact to deliver high-signal findings that pattern-matching scanners miss. Use the Code Review Agent for PR comments and checks within moments of a push. Enforce guardrails with Natural Language Code Policies, written in plain English and executed by the Custom Policy Agent on every PR. Run DeepScan Agent for an on-demand full-repo assessment in about an hour, and use Code Insights Agent to see trends and risk across repos. -
7
WhyLabs
WhyLabs
Enable observability to detect data and ML issues faster, deliver continuous improvements, and avoid costly incidents. Start with reliable data. Continuously monitor any data-in-motion for data quality issues. Pinpoint data and model drift. Identify training-serving skew and proactively retrain. Detect model accuracy degradation by continuously monitoring key performance metrics. Identify risky behavior in generative AI applications and prevent data leakage. Protect your generative AI applications are safe from malicious actions. Improve AI applications through user feedback, monitoring, and cross-team collaboration. Integrate in minutes with purpose-built agents that analyze raw data without moving or duplicating it, ensuring privacy and security. Onboard the WhyLabs SaaS Platform for any use cases using the proprietary privacy-preserving integration. Security approved for healthcare and banks. -
8
Tumeryk
Tumeryk
Tumeryk Inc. specializes in advanced generative AI security solutions, offering tools like the AI trust score for real-time monitoring, risk management, and compliance. Our platform empowers organizations to secure AI systems, ensuring reliable, trustworthy, and policy-aligned deployments. The AI Trust Score quantifies the risk of using generative AI systems, enabling compliance with regulations like the EU AI Act, ISO 42001, and NIST RMF 600.1. This score evaluates and scores the trustworthiness of generated prompt responses, accounting for risks including bias, jailbreak propensity, off-topic responses, toxicity, Personally Identifiable Information (PII) data leakage, and hallucinations. It can be integrated into business processes to help determine whether content should be accepted, flagged, or blocked, thus allowing organizations to mitigate risks associated with AI-generated content. -
9
XBOW
XBOW
XBOW is an AI-powered offensive security platform that autonomously discovers, verifies, and exploits vulnerabilities in web applications without human intervention. By executing high-level commands against benchmark descriptions and reviewing outputs it solves a wide array of challenges, from CBC padding oracle and IDOR attacks to remote code execution, blind SQL injection, SSTI bypasses, and cryptographic exploits, achieving success rates up to 75 percent on standard web security benchmarks. Given only general instructions, XBOW orchestrates reconnaissance, exploit development, debugging, and server-side analysis, drawing on public exploits and source code to craft custom proofs-of-concept, validate attack vectors, and generate detailed exploit traces with full audit trails. Its ability to adapt to novel and modified benchmarks demonstrates robust scalability and continuous learning, dramatically accelerating penetration-testing workflows. -
10
Raven
Raven
Raven is a runtime application security platform designed to protect cloud-native applications by operating directly inside the application during execution, rather than relying on external defenses. It provides real-time visibility into how code actually runs, allowing it to understand execution flows, libraries, and function-level behavior in order to detect and stop malicious activity before it occurs. Unlike traditional tools such as WAF or EDR that monitor from the outside, Raven embeds itself within the application, enabling it to prevent exploits, supply chain attacks, and zero-day threats even when no known vulnerability or CVE exists. It continuously monitors runtime behavior, identifies abnormal patterns or misuse of legitimate logic, and responds immediately to block harmful execution. It also helps teams prioritize security efforts by filtering out the majority of irrelevant vulnerabilities and focusing only on those that are truly exploitable. -
11
Simaril
Simaril
Silmaril is a self-healing prompt injection defense designed to protect AI systems from increasingly complex, multi-step attacks that traditional guardrails fail to stop. It operates by wrapping inference calls and evaluating whether an execution sequence is leading toward a harmful outcome, rather than simply filtering inputs. It uses a multihead classifier that analyzes user intent, application context, and execution states together, enabling it to detect indirect injection, multi-turn attack chains, context poisoning, and tool abuse before damage occurs. Silmaril continuously strengthens its defenses through autonomous threat hunting agents that probe systems, discover vulnerabilities, and generate synthetic training data from real attack scenarios. These insights are used to retrain the model automatically, deploying updated protections in under an hour and propagating anonymized defenses across all deployments.
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