5 Security Measures for AI

Gone are the days when we needed humans to resolve all our problems and perform everyday tasks. Artificial intelligence has taken over the world, bringing in ingenious, innovative techniques and business models that increase the scope of man’s own invention – efficiency and productivity. This makes it crucial to protect AI systems, as it stores all users’ data and personal information.

However, protecting a system that has the ability to alter its own behavior depending on the environment is a challenge in itself, especially as we’re only just beginning to understand the full extent of its application in services. But before we despair, know that if certain steps are taken efficiently, AI security is possible.

This article analyzes five of these steps to maintain a safe, strong and sturdy AI system.

1. Talk to the team.

This is a key step in safeguarding an AI system. You should talk to your team on a regular basis to ensure that they have well-developed and up-to-date concepts of security. Every member should have a firm grip on basic concepts like data classification, privacy principles and data protection techniques. Each one should understand and be able to implement the security measures to keep the system intact and protected, and each individual should be adept at the part they play.

Since data is circulated widely in AI systems, your business should have an efficient data governance structure and ensure that your team is familiar with it. Everyone should be able to take ownership of their work and be graciously accountable for their role in working and securing the system.

2. Execute threat modeling.

Threat modeling is used to optimize network security by first identifying the vulnerabilities and then devising steps to mitigate the effects of potential threats that can harm your business’s system.

Threat modeling should be conducted at both the component level and from an end-to-end perspective, ensuring that security is integrated into the system from the very beginning and that security requirements are met at each point where data is stored and transferred. Interfaces and boundaries between the different subsystems should be scrutinized to perfectly verify assumptions made by those interfaces. Most importantly, all workflows should be inspected in the threat models to ensure nothing is left out that could harm the security of the system in any way.

3. Utilize foundational security functions.

Foundational security functions protect your AI system at all phases of operation, from when it powers up to when it is offline. These are some of the functions you can utilize to maintain the security of your AI project. 

Secure bootstrap

This ensures integrity of the system, using the cryptographic signature on the firmware to guarantee that when the system comes out of reset, it does exactly what the manufacturer intended it to do in the first place, instead of following what a hacker has altered. Such a system specifically protects the root public key, ensuring that it can’t be altered. Because the root of trust identity becomes unforgeable this way, it is impossible for hackers to alter the working of the system and/or steal data.

Key management

Key management protects the keys to ensure that encryption algorithms are not endangered. Your business needs to make sure that the secret key material stays inside a hardware root of trust, because the policies there only allow application-layer clients to manage the keys indirectly through application programming interfaces (APIs). To ensure continued protection of these secret keys, it’s imperative to authenticate the importing of keys and to wrap exported keys.

Secure updates

Since AI adapts to its surroundings, it gets more powerful and sophisticated with time. This is why data and models should be updated continuously, while new models should be protected with end-to-end security. To see your AI system evolve and remain secure from glitches and vulnerabilities, you should update it regularly through this foundational function.

4. Take advantage of transport layer security (TLS).

A transport layer is responsible for end-to-end communication over a network, enabling communication between application processes on different hosts. By providing authentication and identification, it ensures that only secure and authenticated data flows between the systems. Because this prevents the inputs to the neural network from being altered, it guarantees that AI models are not tampered with at any stage of their running. Plus, it manages error correction, allowing the host to receive error-corrected messages and data, thereby providing accuracy and excellence to the end user. Apart from securing your AI project, TLS is well adapted to provide several other services, including flow and traffic control, same order delivery, data integrity, byte orientation, connection-oriented communication, and multiplexing.

5. Check the system regularly.

Last but not least, keep a close and consistent check on the working of the system to ensure that it remains safe and secure. Make certain that you know who can change the build/release environment of your deployment (CICD) pipeline and that your production setup is firmly locked to prevent other people from making configuration changes.

Secure all the software components, confirming that they are at their latest security patch level. It is a good idea here to rotate keys constantly and conduct periodic access reviews regularly to guarantee that no one person has access to all your secret and important information for a long time. Such a rotation guarantees that your data remains secure from hackers and other programs that can hamper the integrity and security of your system. Finally, make sure that you are well prepared and have a dependable response plan in case someone or something is able to breach the security of your AI project or it does something it is not manufactured to do on its own.

Bottom line

It would be a pity if people stop using or investing in AI projects because of the fear of their inability to secure it. While securing AI systems may seem almost impossible, it is not as unattainable as it seems. Truth is, like it or not, the business world has already implemented AI into various aspects of our lives – in how we search for things, the adverts we receive, the products we purchase, how we network and associate with each other, and so much more.

It becomes our duty, then, to understand how AI works and how to protect it, maximizing its application to elevate industry standards and individual quality of life. By taking necessary measures such as using threat modeling and foundational security functions, implementing regular system checks, and educating the people around us on basic security protocols, we can integrate efficient, reliable and secure AI systems to improve our work and personal lives.

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