AI is the frontrunner in reshaping industries from healthcare to defense and beyond. As its impact grows, the need for strong frameworks has never been more critical. While AI systems, are very powerful, it’s also vulnerable to a wide range of security threats and issues, such as data poisoning, adversarial attacks, and model invasion.
According to Article 35 of GDPR, companies must perform a Data Protection Impact Assessment(DPIA) when processing data containing innovative technologies such as Machine Learning and artificial intelligence. This requirement underscores the importance of implementing security measures in AI systems to avoid being non-compliant. In this blog, we will discuss 10 tools to improve the security of AI systems.
1. IBM AI Fairness 360
AI systems can become biased if trained on skewed or non-representative data. These biases can cause significant security implications, especially when the model is used for decision-making or analytical processes. IBM’s AIF360 tool kit helps developers identify and rectify the bias in AI models to ensure fairness.
Key Features
👉A comprehensive set of algorithms for bias detection and mitigation in AI systems.
👉Fairness metrics
👉Bias mitigation techniques to retrain the AI models with corrected data
👉Integration with other AI security frameworks
2. Adversarial Robustness Toolbox (ART)
Adversarial Robustness Toolbox (ART) is a Python library built by IBM and hosted by Linux foundation AI and data foundation for machine learning security. ART supports almost all the popular machine learning frameworks.
Key Features
👉Techniques for adversarial training, defensive distillation, and model regularization
👉Support every popular machine learning framework like TensorFlow, PyTorch, and Keras.
👉White-box and black-box detection methods.
3.AI Model Watermarking Tools
One of the risks in AI is the intellectual property (IP) threat. Model watermarking is one of the techniques to combat intellectual property theft. The goal is to insert a unique identifier that is non-removable into these machine-learning models. These watermarks act as proof of ownership and can be used to determine if it’s stolen or used without authorization.
Key Features
👉Detects whether a model is altered or reverser-engineered
👉Useful to establish an audit trail
👉Supports both deep-learning and traditional machine-learning models
👉Can be easily integrated into the pipeline with a minimal loss in accuracy.
👉Compatible with machine learning frameworks like Scikit-learn, Pytorch, etc.
4.Differential Privacy(DP) Tools
Differential Privacy is a technique that ensures the privacy of the individual data points used in AI models by adding noise in a controlled manner to datasets. It allows AI models to learn from datasets without exposing specific details about any individual, ensuring compliance with regulations like GDPR.
Key Features
👉Provides privacy guarantees for individuals’ data
👉Supports privacy-preserving models
👉Useful for distributed model training
👉Offers protection against re-identification attacks
5. NB Defense
NB defense is an open-source tool to protect AI and is an AI vulnerability management tool. It is a JupyterLab extension and one of the tools that offer vulnerability management at the source of the model development in the early stage. The NB defense CLI facilitates the entire scan of the GIT repo or folders.
Key features
👉It evaluates the notebooks for leaked credentials, PII discloses, and licensing issues.
👉NB defense also performs vulnerability scanning for dependencies used in the project.
👉It also detects non-permissive licenses in ML OSS frameworks and packages.
6. Privacy Meter
Privacy meter is a Python library developed by NUS Data Privacy and Trustworthy Machine Learning Lab. AI models often need large amounts of data to get accurate results. Data leakage during training is one of the common threats faced by developers. The privacy meter offers a quantitative analysis and identifies the training data records that are at high risk of getting leaked.
Key Features
👉It can audit various ML algorithms including those used for natural language processing, vision, and classification.
👉Generates risk estimates based on the simulated attack.
👉Identifying the cause of leakage and performing DPIA for GDPR.
7. AI-Exploits
AI-exploit is a collection of exploits related to AI and offers a pre-configured real-world scanning template based on vulnerabilities. Security teams can use this to test their AI applications and policies against these collected real-world scenarios to bolster against threats. It also helps you to vet third-party providers.
Key Features
👉Identify supply chain vulnerability
👉Scans for vulnerabilities using pre-built tools where each tool is designed to exploit the vulnerability.
8. Garak
Garak is the free vulnerability scanner for the large language model that checks for hallucination, data leakage, prompt injection, and jailbreaks. It is similar to Metasploit for web applications.
Key Features
👉Garak can be integrated with various large language models(OpenAI, cohere, etc).
👉It is accessible via REST API
👉It can detect vulnerabilities and identify the performance capabilities of the AI chatbot.
9.CalypsoAI
CalypsoAI is an enterprise-grade security engine that offers comprehensive protection for AI systems across various use cases and is mainly focused on ensuring regulatory compliance and data protection laws. Calypso AI’s standout feature is its ability to prevent protected information from being entered into external LLMs.
Key Features
👉It protects against prompt injection attacks.
👉It also blocks attempts to bypass the model and uphold the integrity of the AI systems.
👉It also blocks the leak the sensitive information like PII, passwords, and other sensitive data.
10.LangCheck
Langcheck is an open-source automated tool developed by Citadel AI for evaluating a Large-Language model with a Python interface. Langcheck can automatically generate test inputs to trigger vulnerabilities in the system and evaluate the results.
Key Features
👉Langcheck can perform red teaming for LLM.
👉It also checks for factual consistency checks.
👉It also finds grammar errors and checks for fluency in vocabulary.
Conclusion
Securing AI is a multifaceted challenge for companies and developers that requires a combination of multiple tools, techniques, and frameworks. The tools listed above can address issues such as data privacy, model fairness, and intellectual property protection. By using these cutting-edge tools along with best practices, companies can make their AI systems secure, trustworthy, and resilient to modern malicious threats.
Related Reading:
1.AI vs. Hackers: Who’s Winning the Battle?
2.Top 5 LLM’s for cybersecurity Use Case
