What’s the best AI security tool?
It’s a tricky question to answer. If you Google it, you’ll see a heavy focus on security operations (SecOps) tools powered by AI but not nearly enough information on SecOps tools for AI applications.
Of course, it’s important to be up-to-speed on AI-powered cyber security tools, but you also need to know your options for securing AI. That said, we’ll share three popular tools and their broad artificial intelligence security capabilities, plus five more open-source tools and how they align with specific machine learning (ML) attack phases.
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3 popular AI security tools: What you should know
How do the top AI security tools compare? Below, we’ll examine how they stack up to one another, including specialized use cases and limitations to be aware of:
1. Wiz
Wiz is a unified cloud security platform that helps organizations identify, prioritize, and mitigate risks across cloud environments, including AI and ML workloads. Our AI security posture management (AI-SPM) solution secures AI models, pipelines, training data, and services within cloud environments.
As a cloud native application protection platform (CNAPP), Wiz also offers agentless, seamless integration with major cloud providers. Because of this, it delivers full-stack visibility, risk assessment, and proactive security controls for both traditional and AI-driven infrastructure.
Wiz has helped numerous customers, like Genpact, achieve 100% visibility into large language models (LLMs), vulnerabilities, and more—even across multi-cloud environments. We’ve also cut the time to remediate zero-day vulnerabilities down to within 7 days.
Additionally, Wiz’s AI-SPM enables secure AI application development from the start, so our customers have lower risk overall.
Key features:
Agentless AI asset discovery: Wiz automatically inventories all AI services, technologies, and SDKs in your environment to reduce blind spots and help you govern and secure all AI assets.
Comprehensive and automated risk assessment: Our CNAPP continuously evaluates AI pipelines for misconfigurations, vulnerabilities, and data-specific risks, such as unauthorized access or adversarial inputs.
AI security dashboard: Wiz provides a consolidated, prioritized view of AI security risks, which empowers developers and security teams to quickly address the most critical issues.
Limitations:
Wiz’s AI security features work best with mainstream cloud AI services (like AWS SageMaker or Vertex AI) and common SDKs, so you may need additional tooling if you have a bespoke AI stack or a complex hybrid environment.
2. Adversarial Robustness Toolbox
The Adversarial Robustness Toolbox (ART) is the LF AI & Data Foundation’s Python library. With it, researchers and developers can assess, defend, and verify the security of ML models against a wide range of adversarial cyber threats.
Since ART supports all major ML frameworks and data types, it’s a versatile resource for enhancing AI systems’ robustness to protect them from attacks. That includes everything from evasion and poisoning to extraction and inference.
Key features:
Attack and defense modules: ART supports 39 attack modules (such as evasion, poisoning, extraction, and inference) and 29 defense modules (like preprocessors, detectors, and trainers) to enable model evaluation and hardening.
Broad framework and data support: This tool is compatible with more than 10 major ML frameworks, including TensorFlow, and also supports images, tables, audio, and video.
Robustness metrics and certification: ART offers metrics, certification tools, and verification tools for measuring model resilience objectively and reporting on it reliably.
Limitations:
ART specializes in adversarial robustness, so you’d need other tools to address broader AI security concerns, such as data privacy, compliance, and secure deployment in cloud native environments.
The learning curve may be steep if you’re newer to adversarial ML or security testing.
3. Purple Llama
While it’s a newer solution, Meta’s open-source initiative, Purple Llama, is quickly gaining attention. It provides a comprehensive suite of tools and evaluations for building safer, more responsible generative AI models—especially LLMs.
Purple Llama brings together cybersecurity benchmarks, input and output safeguards, and content moderation tools to standardize and advance trust and safety practices in the open AI ecosystem.
Key features:
Llama Guard: As a pretrained model for input and output filtering, this feature detects and blocks potentially risky or policy-violating content before it reaches end users.
Prompt Guard: This functionality secures prompt inputs to prevent prompt injection and related cyber attacks.
Code Shield: Purple Llama evaluates and filters AI-generated code for security issues to protect the integrity of model interactions.
Limitations:
Purple Llama’s tools are mainly for LLMs and coding assistants, with less coverage for other types of AI systems (like vision models or reinforcement learning agents).
It’s a fairly new project that’s still evolving, and some organizations may want more mature or specialized solutions.
GenAI Security Best Practices [Cheat Sheet]
This cheat sheet provides a practical overview of the 7 best practices you can adopt to start fortifying your organization’s GenAI security posture. Inside you’ll find:
4 additional AI security tools and how they align with ML attack phases
You’ve gotten an overview of some popular security-focused AI tools—but there are, of course, others available.
Though each of the four tools below may have broader capabilities, this list will focus on how each tool aligns with a specific ML attack phase. No matter what tools you evaluate, though, zooming in like this will give you a better idea of what capabilities to look for so that you’re prepared for any and every attack phase.
Looking for commercial tools? Check out our review of the most popular AI security solutions.
Let’s take a closer look:
1. NB Defense: Reconnaissance and initial access phase
NB Defense is a purpose-built tool for Jupyter Notebooks, which is a common entry point in ML and AI development. Available as both a JupyterLab extension and a command-line interface, NB Defense helps data scientists and ML engineers identify and remediate various security risks directly within notebooks or across entire repositories.
Key features:
Secrets detection: NB Defense identifies hidden API keys, authentication tokens, and other sensitive credentials in notebook code or outputs.
PII and sensitive data scanning: This tool scans for personally identifiable information (PII) and other sensitive data in code and outputs.
Dependency vulnerability scanning: It also scans for vulnerabilities in imported ML frameworks and libraries.
Limitations:
NB Defense primarily performs static analysis on notebook content and dependencies rather than monitoring runtime behaviors or detecting active exploitation attempts.
Since this tool is for Jupyter Notebooks and their immediate dependencies, it offers less coverage for other AI and ML development assets like scripts, containers, and non-notebook pipelines.
2. Garak: Model manipulation and evasion phase
Garak is a specialized framework for LLM and AI agent red-teaming and security assessments. It systematically probes models using various adversarial techniques to uncover vulnerabilities, from data leakage to jailbreaks and beyond. It’s popular among security researchers, developers, and AI ethics professionals who need to automate the discovery and reporting of weaknesses and security risks.
Key features:
Adaptive attack generation: Garak uses a flexible framework with generators, probes, detectors, and buffs to create and adapt attack strategies based on model responses.
Plug-in–based extensibility: This tool allows users to develop and integrate custom probes and detectors for specialized attack scenarios.
Extensive model compatibility: Garak supports integration with many LLM providers—including OpenAI, Hugging Face, Cohere, and Replicate—and also supports custom Python models.
Limitations:
Garak is mainly for language models and dialog systems, so there’s limited support for non-LLM AI models (like vision or structured data models).
This solution is great for identifying vulnerabilities, but unlike other tools, it doesn’t automate defenses or implement real-time protection mechanisms.
3. Privacy Meter: Data poisoning and supply chain attacks phase
Privacy Meter is a Python library that helps you audit and quantify ML models’ privacy risks. However, it mainly focuses on risks related to training data information leakage.
This tool primarily uses state-of-the-art membership inference attacks to assess how much sensitive information is at risk of exposure from a model. Its goal is to identify and mitigate privacy threats throughout the AI lifecycle, including risks relevant to data poisoning and supply chain attacks.
Key features:
Aggregate and individual privacy risk reporting: Privacy Meter provides detailed reports that score both overall and per-record privacy risk so you know which data points are most vulnerable to leakage.
Flexible threat modeling: This tool allows you to assess privacy risks under different attacker capabilities and access levels, including black box, white box, and federated learning scenarios.
Automated privacy risk visualization: It also automatically plots ROC curves and quantifies the area under the curve to represent the likelihood of successful attacks.
Limitations:
This AI security solution performs static analysis on trained models and datasets, so it won’t be the top option if you need ongoing, real-time monitoring for data integrity or supply chain threats during model training or deployment.
Privacy Meter only measures privacy risks and information leakage, so you’ll need another tool to directly detect and mitigate data poisoning or backdoor cyber attacks.
4. Viper: Post-exploitation and lateral movement phase
Viper is a red team platform for adversary simulation and security assessments across all phases of the MITRE ATT&CK framework. It features an extensive library of post-exploitation modules, automation capabilities, and support for multi-platform operations. This makes it a good fit for simulating and analyzing post-exploitation and lateral movement scenarios in both traditional and AI-driven environments.
Key features:
Post-exploitation module library: Viper’s platform contains over 100 built-in modules that cover persistence, privilege escalation, credential harvesting, lateral movement, and more.
Custom module extensibility: It supports Python-based plug-in development and customizable workflows to tailor post-exploitation techniques and lateral movement simulations to your org’s needs.
AI-powered LLM agents: Viper offers LLM agents so your security team can make intelligent decisions and employ automation to make red teaming more efficient.
Limitations:
This platform is for adversary emulation and assessment, so it’s not an active monitoring or prevention solution.
Viper is best for organizations that have experienced red teamers or security professionals who can get up to speed quickly on how to leverage its capabilities.
What to look for in an AI security solution
There are many tools to choose from, and each has its own strengths and weaknesses. However, taking the following criteria into account will help you choose a well-rounded solution that will strengthen your security posture:
Integration with cloud and DevOps pipelines
Any good AI security tool will integrate seamlessly with CI/CD pipelines to provide automated monitoring, advanced threat detection, and remediation throughout the software development lifecycle. After all, it’s critical to identify and address vulnerabilities early to cut down on integration issues and minimize the risk of misconfigurations that could expose sensitive data.
Wiz, the first CNAPP to offer AI security posture management (AI-SPM), is a great example of a tool that integrates with both cloud native environments and DevOps workflows. It enables full-stack visibility and automated misconfiguration detection in both AI services and infrastructure as code.
Regulatory readiness and compliance support
Your AI security platform of choice should help you meet regulatory requirements like GDPR and CCPA, as well as other industry-specific standards. This includes automating compliance checks, identifying sensitive data, and providing audit trails.
For instance, Wiz supports compliance automation for NIST and other frameworks. It also provides tools that automatically detect and protect sensitive training data.
Comprehensive visibility and shadow AI detection
Your security team needs tools that automatically discover all AI assets—including models, services, and SDKs—across your cloud environments to prevent shadow AI (unauthorized or undocumented AI usage). In other words, you need an inventory of AI resources, or an AI bill of materials (AI-BOM), to reduce the risks that come with unmonitored AI deployments.
Misconfiguration and vulnerability management
Look for tools that enforce secure configuration baselines, ideally with built-in rules for AI services, to save your security team work. A good tool should also continuously scan for misconfigurations and prioritize remediation based on risk so your team can focus on resolving the most critical issues first.
Attack path analysis and proactive risk mitigation
Being able to respond quickly to security incidents is essential, but your main goal should be to prevent as many potential incidents as you can.
The most effective AI security solutions are also the most proactive. They identify and eliminate attack paths to AI models and data using contextual analysis across cloud workloads, identities, and network exposures.
Boost your AI security with Wiz
Most security teams don’t love the idea of adding several more solutions to their tool stack. If that’s true of your team, too, the ideal scenario would be to find a comprehensive AI security solution that ticks most, if not all, of the above boxes.
For countless companies, Wiz’s AI-SPM is that solution.
Wiz offers full-stack visibility into your AI pipelines via an AI-BOM and enforces secure configuration baselines with built-in rules that detect misconfigurations in your AI services. Additionally, it helps you proactively discover and remove critical attack paths related to AI models and training data with accurate risk prioritization.
To get a closer look at what risks AI-SPM can detect and how to address them with Wiz, grab our AI security assessment sample report. Alternatively, for more personalized details on the current state of your AI security and how to improve it, schedule a free 1:1 AI security assessment today.
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