| Contributor
AI has opened up many doors for businesses and individuals alike, but those open doors sometimes let bad actors follow close behind.
AI systems, programs, and platforms are perhaps some of the most transformative tools to have entered mainstream industries in the past five years. As useful as many AI tools have been, however, they’ve also introduced or otherwise exacerbated several problems, with one of the most important being security. This issue has necessitated the development of AI security measures.
What is AI Security?
AI security doesn’t refer to a single program or practice, but instead a series of processes designed to work in conjunction with each other to limit unauthorized access to an organization’s AI systems, data pipelines, models, users, permissions, cloud infrastructure, and connected applications. Though many of these processes incorporate AI in some way, some benefit from human oversight and intervention.
For example, while an organization may use AI to improve their threat detection and incident response programs, they may still rely on a cybersecurity team to actually address the issues those programs flag.
Ultimately, both AI and security experts work together to enhance an organization’s security posture by minimizing its exposure to external and internal threats. How a given security team approaches this may differ depending on the tools they have available to them and the amount of data they need to protect; a healthcare enterprise tasked with keeping patient records safe will likely invest more in AI security than a small bakery, for instance.
How AI Security Works to Keep Sensitive Data Safe
Given how many industries have adopted AI to some degree—customer service, software development, document review, marketing—AI security has become as much of a business governance issue as it is a response to technical vulnerabilities.
For instance, according to the 2025 “Cost of a Data Breach Report,” the global average cost of a data breach was 4.4M USD. Meanwhile, organizations that used AI security extensively saved an average of 1.9M USD in repairs. AI security options, then, have become an attractive means of both mitigating risk and reducing costs associated with damages.
Recall, however, that AI security isn’t simply a matter of having the right tools. It also relies heavily on adherence to strong data management practices, something both AI tools and human users have to be aware of as they engage in routine operations.
This is particularly true for companies that frequently handle large amounts of sensitive data, such as customer records, source code, contracts, and employee data, to name just a few items. Unfortunately, having more data to protect sometimes means there’s a greater likelihood it will be mishandled.
If, for instance, a product team were to upload customer feedback into an AI assistant to summarize feature requests, there’s a chance that dataset would contain personal information or contract terms. Without the right controls for access and logging in place, it’s possible that data will inadvertently be stored in a place where hackers could get to it.
One might blame this leak on faulty security measures, but keep in mind that someone would’ve needed to properly configure those security measures in the first place. In other words, AI security doesn’t start with tools, but instead an understanding of what data is being used and who can access it at any given time.
Locating Practical Frameworks for Managing AI Risk
Since the popular use of AI and AI security is still fairly new, understanding how to implement AI security or evaluating which tools to use has yet to become common knowledge among security teams. To address this gap in knowledge, the National Institute of Standards and Technology (NIST) released a practical framework in July 2024 to help organizations identify risks unique to generative AI.
This framework is purposefully technical, so to put it in more accessible terms, it effectively states that businesses should first map where they use AI before classifying the data those systems can access.
Businesses should then measure and manage relevant risks across a piece of software’s development and deployment cycle. Once the software is released, businesses should review its AI’s behavior and document controls/accountability to create data samples for future comparisons.
Simply put, all of these steps and processes boil down to making sure that one’s AI security protocols are repeatable, documented, and tied to real workflows.
AI Security and Responsible Adoption
AI security’s complexity and scope can cause some organizations to wonder whether using AI is truly worth the trouble. In truth, AI tends to solve more problems than it causes when properly implemented and protected since it opens up possibilities for innovation and efficiency.
Still, strong AI security requires diligence and an understanding of how an organization’s data interacts with various users. Carefully putting the right data governance and identity controls in place can make for a good first few steps toward securing peace of mind.
The information provided in this article is for general informational and educational purposes only. It is not intended as legal, financial, medical, or professional advice. Readers should not rely solely on the content of this article and are encouraged to seek professional advice tailored to their specific circumstances. We disclaim any liability for any loss or damage arising directly or indirectly from the use of, or reliance on, the information presented.
