Artificial intelligence (AI) has made its way into nearly every facet of running a small or mid-sized business in the modern age. When programmed appropriately, AI can improve response time and catch security threats before they become a problem. Unfortunately, AI inherently comes with the potential for biases and can skew algorithms in strange ways.
Ways AI bias increases cybersecurity risks
Examples of AI biases gone wrong are obvious when it occurs. AI missed the mark in early 2024 in significant ways, including Google’s Gemini creating an image of U.S. founding fathers as women and various races, which was historically inaccurate.
However, AI also increases cybersecurity risks when it makes false assumptions. A biased machine could see legitimate activities as threats and disrupt usage. The system may produce false negatives and eventually adapt and allow bad players inside. AI isn’t above targeting user groups and watching them more closely than others — because AI is built by people.
While AI is a valuable tool for monitoring cyberattacks, users must be aware of its limitations, and human workers should monitor it to avoid any serious transgressions due to system biases.
How to avoid AI bias in cybersecurity efforts
Making a few small changes can improve the way AI monitors and eliminates system threats. Here are some ways to reduce AI bias in security:
1. Use small language models
Datasets of large language models rely on massive amounts of information. It might be best to create several small language model sets and deploy each to cover a single aspect of security.
As a result, you can use fewer resources and create AI programs that can take on more specialized tasks, such as focusing specifically on watching for SQL injection attempts.
2. Focus on diversity
While small language models work well, diverse datasets are crucial for bigger or more comprehensive operations. Ultimately, a machine doesn’t have the same ability to reason as a human. While a statistic may show a pattern, humans can use critical thinking to understand it is not an indicator of every person in a gender or race.
3. Train staff
One study shows that a mere 10% of the world’s workforce has AI skills needed in the future. Since humans are crucial to successfully using AI without inherent biases, educating staff on how to add to AI language models and work within the parameters of what’s possible is critical to successfully integrating AI into cybersecurity.
Start with your IT team, as they’ll handle security tasks. However, all employees should eventually be trained to launch the proper parameters for machine learning. Give them the tools to know when to utilize AI and when to stop it in its tracks. Real-life examples, role-playing and experience in cybersecurity help.
4. Implement bias detection
Utilize bias detection tools to identify prejudices in the system. When AI focuses solely on a group of people or actions, it can show false positives while ignoring real threats.
Adjust programming to implement fairness constraints. Once the tool identifies biases, work to remove them from the models. Systems must be audited frequently and tested by humans to remove issues. Adding people to the process helps you locate and remove any biases before they become security holes hackers can exploit.
5. Deploy advanced threat identification
Advanced detection allows companies to react quickly to cyberattacks and avoid data breaches. Attack simulations can train machines to better recognize malicious users and reduce incident response rates.
The better AI understands the uses and patterns of the system, the faster it will identify anything unusual. While the machine sometimes exhibits false positives, human monitoring allows you to correct any errors and allows the program to better learn what constitutes a threat.
Tap into the power of explainable AI networks, so your IT team can understand the way AI models make decisions on what is a threat. The more you study how the decisions happen, the better you’ll be able to identify biases and weaknesses and fix them.
6. Diversify your data
Humans come with biases, whether they want to admit it or not. Adding extreme caution to the things you add to your training models can make a difference in the biases AI develops.
A team of researchers works best when they come from different world views. Monitoring the data and ensuring there aren’t inherent biases already built into information can make a difference in how well the program functions to protect your organization from hackers while keeping access available to employees and customers.
Embrace human ethics for machines
Training AI models to eliminate bias comes from human interaction and letting the machine know what is appropriate. To avoid creating unintended consequences, you must have a diverse team monitoring one another and creating datasets over time.
Only with respect and understanding of one another can you create an AI program that avoids bias and functions as intended to protect employees and stakeholders from the impact of a cybersecurity event.
