AI has almost reached the point of becoming as ubiquitous to an organization’s IT system as cloud computing. In fact, a Microsoft study has found that three-quarters of knowledge workers (75%) use generative AI. If your employees aren’t already using a generative AI application now, they will be soon enough.
Because AI is only as good as the data used to train it, IT and security teams are faced with the challenge of ensuring the data is not only high quality, but secure and meets compliance regulations. And it isn’t just data that needs to be prepared for AI—the infrastructure needs to have the necessary components to manage and deploy AI systems efficiently and securely.
To securely ready your data and infrastructure for AI, consider the following 8 steps:
- Conduct a Data Audit: Knowing what data you have and where it is located is the first step. Organize a full evaluation of your organization’s digital landscape, including every device and system where data is generated, transmitted and stored. This will include cloud infrastructures, IoT devices, and any social media or Web applications.
- Conduct an Infrastructure Audit: The second step is to know your infrastructure just as intimately. An audit or penetration test will help you find weak spots and vulnerabilities that could be exploited through AI systems. This will also help you set up zero-trust architecture.
- Data Quality Assessment and Classification: Once you have a complete view of your organization’s data, the next step is to assess its quality and classify the information. This involves establishing a data catalog through a robust, agile data intelligence/data management and integration solution. Key considerations in choosing the right solution include processes for data classification, normalization, and movement. Data quality assessments should focus on consistency, accuracy, completeness, uniqueness, and timeliness, allowing you to classify data into categories such as restricted, confidential, private, and public. Proper classification helps protect sensitive and proprietary information from being exposed or used in AI models.
- Data Cleansing: Because the data going into AI models needs to be as accurate and truthful as possible, it should be rid of any inaccuracies and inconsistencies. According to an Experian report, 85% of organizations said poor data quality has a negative impact on operational processes and efficiencies. With data cleansing, AI models are better informed to make unbiased decisions and streamline business operations for overall better performance.
- Data Governance and Compliance: Guidelines are needed to ensure data is used ethically and securely while remaining compliant with federal and industry regulations. This includes clear definitions of data ownership. Governance also includes data security measures such as encryption, access privileges, privacy policies, and endpoint protections. Because data governance over time has likely not been adhered to, the company should consider adding tools to proactively analyze data to identify sensitive information and taking actions including removal and data classification. These tools will ensure copies of data are tightly managed for access and removal once the purpose for the information is completed; this is especially important for those organizations that have development environments where data sets are often copied as part of the development process.
- Threat Modeling: Use threat modeling to identify potential risks and vulnerabilities in AI, and use that information to develop best security practices to assess threats and incident response plans across the infrastructure using AI. If exposed to poor quality and untrustworthy data, NIST warns that AI can fail, and there is an uptick in threat actors exploiting this problem. Threat modeling offers a layer of protection to vulnerabilities in AI models.
- Data Usage Security: Some of generative AI’s biggest security flaws are due to humans entering sensitive and proprietary information, not realizing that once in the AI model, it can be accessed by anyone. Organizations need to place tight controls over the data used in AI. AI security awareness training is also necessary.
- Continuous Monitoring: Preventive measures don’t stop once the AI model is running. Continuous monitoring will continue to look for potential threats before they become problems and identify areas where security measures can be made stronger.
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