New AI-related legislation pieces that will largely impact software houses' approach to their work are The European AI Act and The Blueprint for an AI Bill of Rights.
When different types of AI are growing in popularity and becoming more integrated into various aspects of society, the need to regulate their use for safety reasons becomes increasingly urgent. To avoid potential threats and ensure that AI is used responsibly, software houses, therefore, need to prioritize compliance with these regulations when developing and deploying software.
Although studies have shown that AI-generated code is significantly less secure than human-written code, AI tools are still used in software development. The productivity gains are indisputable, but is it possible to create secure software when using AI?
What will you learn from this article?
How to secure software development when using AI?
Why are AI laws and regulations so important to follow and comprehend?
What is the European AI Act?
What is the Blueprint for AI Bill of Rights?
How do the top EdTech companies use AI in their business?
What are the top AI tools used by software developers?
Best practices of software development
Key Takeaways
Software developers need to stay up-to-date with all AI-related laws and regulations to ensure compliance and avoid legal issues.
When incorporating AI into software development, it is essential to prioritize security, especially when handling sensitive data or personal information.
Each phase of the software development process should include thorough validation of the reliability of the AI tools and the implementation of proper measures to avoid data breaches.
The European AI Act divides AI-based solutions into categories based on the software's potential.
Software development houses will likely need to adhere to even more new regulations in the near future to ensure their products are safe enough to use.
Top EdTech companies are using AI technologies in their business to drive business growth and streamline the learning processes.
Tools like Gemini, ChatGPT, and GitHub Copilot cannot be used without proper oversight in a software house setting. Otherwise, they may pose serious risks to privacy and security.
How Can You Ensure Secure AI Development or Implementation?
There are four fundamental elements of creating secure software when using AI. The basic principles relate to phases of the development lifecycle:
secure design,
development,
deployment,
operation and maintenance.
Everyone involved in the product's creation (including managers and owners) should be educated on secure software development best practices and AI regulations to ensure appropriate security measures to protect against potential threats.
Secure Design
The confidentiality and integrity of both the generative AI system and the software must be ensured through careful planning.
Assess potential AI-specific threats and find a way to mitigate them effectively before you even start working on your software. You should also plan what to do if your software becomes compromised and have a response plan in place in case of a security risk.
Development
Developers responsible for creating the software should be aware of secure coding practices and regularly update their skills and knowledge to stay current with the latest threats and defenses. As cybercriminals might be able to reconstruct the functionality of a model or the data it was trained on, it is crucial to implement robust access control measures.
Assess and monitor the security of its supply chains, requiring suppliers of any outside components to adhere to the same standards as other software. Document the creation, operation, and life cycle management of models, datasets, and prompts, including security-relevant information.
Deployment
During the deployment phase, continuous monitoring and vulnerability scanning are needed to identify potential security risks. A responsible deployment plan should be in place to minimize risks and protect user data.
To protect your model and data from attacks, implement standard cybersecurity best practices and controls on the query interface. Proper access controls, encryption, and monitoring mechanisms to ensure the software operates securely in its intended environment are some of the ways you can safeguard against potential cyber threats and security breaches.
Operation and Maintenance
After deployment, in accordance with the security plan, regular maintenance and updates should be conducted to address any new vulnerabilities that may arise.
Software houses should also log inputs like inference requests, queries, and prompts to comply with privacy and data protection laws, audits, investigations, and remediation in case of compromise or misuse.
Participating in information-sharing communities, collaborating with others in the industry, and sharing your knowledge are also a good idea to build a safe environment for everyone involved in software development.
Artificial Intelligence: Laws and Regulations
In terms of AI, many people worry that unfair or illegal practices may arise due to its range of possibilities. The AI Act in the EU and the AI Bill of Rights in the USA are the guiding principles on how artificial intelligence should be ethically developed and used, aiming to ensure transparency and accountability in AI-related practices. These regulations emphasize the importance of protecting individuals' rights and freedoms.
The European AI Act: What Do You Need to Know When Developing Software?
The Artificial Intelligence Act (AI Act) is a European Union regulation that is meant to regulate the development of AI systems and AI-based technologies. The Act classifies the applications based on the possibility of misuse or harm they may cause, with several different categories:
unacceptable risk (for extremely risky software, for example, meant for manipulating human behavior),
high risk (for critical infrastructure, law enforcement, and similar),
limited risk, and minimal risk.
The AI Act requires software houses to adhere to strict guidelines and standards when designing, developing, and deploying their products. Failure to do so might result in legal action or the software product getting banned. The legislation is expected to have a significant impact on the software development field and will likely affect the way SaaS companies operate.
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Regulations in the US: The Blueprint for an AI Bill of Rights
The AI Bill of Rights outlines five principles to safeguard individuals' rights and privacy in the world of machine learning solutions. By establishing clear guidelines and standards, the AI Bill of Rights seeks to prevent potential harm and discrimination caused by artificial intelligence solutions technologies, such as those meant for social scoring and facial recognition. It is a set of suggested best practices rather than an enforceable document that organizations can voluntarily adopt to make sure their software systems are morally and responsibly constructed.
Safe and Effective Systems
Users should be protected from unsafe or ineffective systems. Software should undergo pre-deployment testing, risk identification, and ongoing monitoring to ensure safety and effectiveness. Protective measures may include not deploying or removing the software if deemed unsafe.
Algorithmic Discrimination Protections
Systems should be used and designed in an equitable manner, and users shouldn't have to work with discriminatory algorithms. Algorithmic discrimination occurs when automated systems unfairly treat people based on their race, color, ethnicity, religion, age, or other criteria. Developers should take proactive measures to protect individuals and communities from algorithmic discrimination.
Data Privacy
Data protection is crucial, and users should have control over their data usage and be protected from privacy violations. Software houses should seek user permission and respect their decisions regarding data collection, use, access, transfer, and deletion. Consent should be brief, understandable, and user-controlled, with enhanced protections for sensitive domains, heightened oversight for surveillance technologies, and respect for data decisions in education, work, and housing.
Notice and Explanation
Users should know how an automated system is used and why it affects them. Automated systems should provide clear, accessible, and timely documentation, including system functioning and explanations of outcomes. Notification of significant changes and public reporting should be made clear.
Human Alternatives, Consideration, and Fallback
Users should have the option to opt out of automated systems and have access to a human alternative when necessary. Automated systems should be assessed for effectiveness and timeliness, and their appropriateness should prioritize public safety.
How EdTech Companies Use Artificial Intelligence in Their Businesses
AI has been reshaping education for quite some time now, with top SaaS EdTech companies leveraging the technology to improve their software development process, streamline content delivery, optimize data collection, and much more. Integrating AI into the learning experiences they provide to users makes their platforms more personalized and adaptive, leading to an increased chance of market success. Let's take a look at some of the top EdTech businesses and how AI systems are being utilized in their systems.
Duolingo
The world's most popular language learning platform, Duolingo, uses AI to personalize lessons for each user based on their unique learning style and progress. According to the company's blog, their machine learning algorithms analyze user data to determine the most effective way to create the best experience for the app's users.
In terms of AI and security, Duolingo makes a deliberate effort to create a secure and fair learning environment, adhering to the most stringent rules and standards to protect user data and privacy.
Additionally, the company executes empirical investigations of the reliability of the AI and evaluates its security measures regularly to enhance threat detection and prevention.
Coursera
Recently, Coursera, the platform known for its online courses and certifications, has introduced Coursera Coach, an AI-powered feature offering a virtual coach that helps the user stay motivated and on track with their learning goals. The Coach tool can provide personalized advice, recommendations, and reminders to keep users committed to their learning goals. Another new feature called the Quick Grader, allows users to receive instant feedback on their assignments and homework.
Skillshare
Another platform using AI in SaaS is Skillshare, an online learning community with thousands of classes. Interestingly, one of the ways in which the company uses artificial intelligence is e-mail marketing: by sending out personalized, targeted e-mails to users based on their preferences and activity on the platform, Skillshare gets more engagement and increases user retention.
Udemy
Udemy has recently revealed a range of generative AI-driven products supposed to revolutionize the way Udemy users learn and interact with course material. The Intelligent Skills Platform will utilize machine learning algorithms to personalize the learning experience and offer better course recommendations to users. It will also include an AI Learning Assistant designed to provide instant feedback and support to students as they progress through their lessons.
Khan Academy
In 2022, the well-known educational platform started experimenting with large language models and generative AI. Khan Academy uses artificial intelligence to generate text, like first drafts of articles for their websites, or ideas for test questions for online courses. Khanmigo, the company's AI-based learning assistant, is a new feature that enhances the overall educational experience by providing personalized support to students. Because the platform values user privacy and overall security and supports ethical AI usage, the features are designed to prioritize data protection and safety.
Secure Software Development: Tips for the Top Tools Used by Developers
As a software development house, we use a variety of AI-powered tools that help us streamline our development processes. We use the following three tools in our work, and we have put in place specific guidelines and procedures to guarantee tight security.
Google's Gemini (previously known as Bard), like any other tool, when used without proper caution can have detrimental consequences for the security. It is recommended that all personally identifiable information be replaced or removed before feeding the AI any data, as well as that the results be reviewed for accuracy, relevance, and security.
ChatGPT, arguably the most popular AI tool of recent years, can generate code, but it should always be used in conjunction with manual code review and testing to ensure the security and quality of the final product. Additionally, to ensure secure development, developers should never input real information in there, especially sensitive or personal data. ChatGPT should be used only with the learning features disabled to avoid any accidental data breaches or leaks.
Another well-known generative AI that is used at our software house to assist developers in their work is GitHub Copilot, created in collaboration with OpenAI. As with the rest of machine learning-based tools, it is important to use GitHub Copilot responsibly and never input real, sensitive information into the tool.
Always be cautious when using AI-generated code, as it can contain errors that will lead to security challenges. Before using the code in your SaaS solutions, make sure it doesn't contain any elements that might be subject to intellectual property laws or confidential information.
Summary: Secure Software Development
Software development houses should adhere to the suggestions and guidelines provided by the EU AI Act and The Blueprint for an AI Bill of Rights.
It is necessary to put strong security measures in place before even beginning the software design processes. Using secure development practices early on may help avoid problems in the future.
When creating software with the help of AI, developers should follow specific rules to avoid data breaches, monetary losses, and reputational harm to a business.
Therefore, all businesses, whether they're an EdTech company or a SaaS startup, have to keep the whole software development lifecycle secure.
There are many ways in which software development houses can avoid security flaws, but the simplest one is to overview everything. Not feeding the AI models sensitive or private data, always checking for errors, and disabling any learning features. Implementing specific internal guidelines related to AI use for extra security might be a game changer.
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