1. Secure development practices
Now that we've learned about internal and external risks relevant to AI systems, this video covers secure development practices.
2. Counteract the unique vulnerabilities of AI
Think of regular security measures like castle walls, repelling basic attacks. Secure development practices add another layer of defense for AI systems. They are built by the teams that create and maintain these systems. We'll examine at a number of design practices that can be utilized when developing AI systems to help ensure their safety. Let's start by exploring the practices that safeguard data preparation and management.
3. Data provenance and traceability
The first, data provenance and traceability, means understanding the origin of data and tracking its journey through the AI system. It helps ensure the integrity and authenticity of the data being utilized. Data poisoning attacks, for example, can be combated through traceability by pinpointing and removing corrupted data that has been injected into the system.
4. Validation and sanitation
Data validation refers to checking and cleaning data before it is fed into an AI system. This helps ensure the data is accurate, relevant, and free from malicious tampering. For instance, robust data validation processes can help detect and mitigate where attackers might craft input data to mislead the AI model.
5. Data minimization
The third, data minimization, is limiting the amount of data collected and processed to only what is absolutely necessary. This practice can significantly reduce the risk of privacy breaches and data theft.
6. Encryption and anonymization
The final data-specific practices are encryption and anonymization. Encryption transforms sensitive data into a secure format that can only be accessed by authorized parties. This gives it an additional protective barrier against attempts at data theft or unauthorized access. Anonymization techniques further enhance privacy by removing personally identifiable information from datasets. This helps protect against model inversion attacks aimed at extracting sensitive information from the AI system.
Now we'll look at three secure development practices not specifically related to data preparation and management: secure coding, access management, and secure infrastructure.
7. Secure coding practices
Secure coding practices can reduce vulnerabilities within the AI system's code base. Examples include regular code reviews, vulnerability scanning, and adhering to coding standards designed to prevent security flaws. Secure coding can help shield against infrastructure attacks by ensuring the underlying software is robust against exploitation.
8. Access controls
Limiting who can interact with the AI system and its data is also a key practice. Strict access controls help protect against unauthorized access that could lead to data breaches or model theft. Only allowing trusted, verified individuals or entities to modify the AI model or its data is crucial. It significantly reduces the risk of external attacks aiming to exploit the system's vulnerabilities.
9. Secure infrastructure
The final practice we'll look at is secure infrastructure. It helps ensure that both the hardware and software environments are safeguarded against attacks. Examples of this include firewalls, intrusion detection systems, and secure cloud services. They create a solid foundation that protects against infrastructure-targeted threats.
10. Security isn't for the tech team alone
In this lesson, we've learned practices for securing AI and reducing risk. Remember that this task isn't for the tech team alone. Business leaders play a key role by funding security tools and training. Legal experts check that practices respect user privacy. Regulators define additional checks and balances. This teamwork ensures that the AI systems are not only secure but also trusted and reliable.
11. Let's practice!
Now let's deepen your learning with some exercises!