In today’s environment, there are inherent risks to working with your data. Implementing AI security tools, techniques, and processes can ensure that your data remains one of your organization’s most valuable assets, rather than a liability.
Every minute, a new, potentially reputation-shattering attack happens to a business, making headlines that the entire world (read: their customers) will see. You don’t want to be next.
IBM’s 2021 Cost of a Data Breach report showed that automation and security AI had the biggest cost mitigating effect against threats, saving organizations up to $3.81 million more than those without it.
Here’s what you should do to establish a secure AI strategy that actually works.
Building an AI Security Strategy You Can Trust
Secure AI consists of three elements working in tandem—your data, your models, and your data science platform. Let’s break this down a bit further.
Can you trust your data?
There’s more data around than ever before—in fact, though there were 5 exabytes of information created between the dawn of civilization through 2003, that much information is now created every two days, according to Eric Schmidt of Google. It’s essential you protect your data from being compromised so you can build models off “good” data and avoid potential negative business impacts.
The first step is making sure no one has access to your data that shouldn’t. Being GDPR (General Data Protection Regulation) compliant is not only a requirement, it also ensures that internal users are handling data correctly, and that only those allowed to see and use certain data have access to it.
If an enterprise’s data isn’t kept private, valuable business knowledge and sensitive user information can all too easily be exploited. While data privacy isn’t a prerequisite for secure AI, it’s a requirement to build trust with whichever party owns AI development—making it an essential piece of the puzzle.
So, how do you go about doing this?
Data lineage, correct user authentication and authorization, data encryption and anonymization are all effective and required elements—doubling down on controls, erasing or encrypting identifiers that connect an individual to stored data, and understanding how data flows throughout the organization help keep your data more secure.
Federated learning is another great way to work with large amounts of sensitive data and prioritize users’ privacy without reducing speed or accuracy. With federated learning, information doesn’t need to leave the source device—reducing the likelihood of data attribution attacks or interference, especially in a highly regulated industry like insurance or healthcare.
The bottom line is—to build security for your data science program, you need to think of ways to protect your data and build trust in the models themselves.
Can you trust your models?
Similar to the way your data should be kept under lock and key, your machine learning models should be secured, too. Models should only be consumed by those who have authorization to use them.
Without security, models can be taken down, compromised, or even “stolen.” Malicious parties could fire tons of requests at a model in attempts to bring it offline or explore its functionality to gain access to the model owner’s intellectual property by replicating it. This can then derail model ops, impacting ROI monitoring and putting business value at risk.
It’s equally important to build off your established data integrity and ensure model integrity as well—are your models performing the way they’re designed to? If the answer is no, you’re putting business-critical decisions in the hands of a machine you can’t trust.
So, how do you make sure this doesn’t happen?
For model security, authenticating and authorizing model developers to only work on specific models is important. Even more important is to secure consumption of models.
As an example, REST API-based model deployments could be secured via token-based security. Using RapidMiner’s model ops capabilities, you can also use access counts, view response times, and input data distributions to detect irregularities that may indicate abusive behavior like Denial-of-Service (DoS) attacks or similar.
Explainable AI is an excellent way to ensure model integrity—explainable AI can help with regulatory compliance, transparency, and accountability by “profiling” models and certifying they work as expected.
Having secure data and models will set you up with a strong foundation for a trustworthy data science program.
Can you trust your AI platform?
Last but not least, you need to select an AI platform that keeps the data and models you worked so hard to secure, safe.
If you choose a great vendor, you can rest assured that your data is being continuously monitored, your organization’s security protocols are being followed, and you can spend less time worrying about your platform security and more time making an impact with your data.
If you choose a not-so-great vendor, you face the risk of your business’s most sensitive information being exposed and losing control of your team’s best models.
If you go the no vendor route and choose a DIY approach to data science (like Python), you inherit the burdens of a DIY security program. Establishing your own security controls in Python is time-consuming and often more error-prone than using a vendor that’s inherently motivated to establish a high standard for platform and data security.
So, what should you look for when evaluating which AI platform to trust?
First things first, you need the basics—authentication, authorization, encryption, auditing, and data lineage capabilities. As we outlined before, these capabilities are key to establishing a secure process for working with, tracking, controlling access to, and protecting your data.
Be on the lookout for sophisticated access controls, think about Single Sign-On (SSO) and Two-Factor Authentication (2FA), and keep in mind that vendors with a SOC2 certification have proven their effectiveness across five specific trust service principles by an independent third party, giving them an extra vote of confidence.
Why Trust RapidMiner
We don’t want you to waste precious time worrying about the implications of a data breach or ransomware attack—that’s why we offer top-of-line security features and compliance standards as well as boasting a SOC2 certification.
RapidMiner also simplifies access to innovative techniques like federated learning by combining various local models to form a centralized, global one – which has tremendous model security benefits when deployed properly. Manufacturers, for example, can use RapidMiner to calculate models with sensor data on the shop floor, then share the model’s parameters and findings with other agents, without sharing the raw data.
Curious to learn more? Check out our platform security brochure for more detail on our in-depth security controls or request a demo with one of our federated learning experts today.