Artificial intelligence (AI) has made its way into almost every facet of running a small or medium-sized business in the modern era. When programmed appropriately, AI can improve response time and detect security threats before they become a problem. Unfortunately, AI inherently carries the potential for bias and can skew algorithms in strange ways.
How AI Bias Increases Cybersecurity Risks
Examples of AI bias gone wrong are obvious when they happen. AI missed the mark in early 2024 significantly, including Google’s Gemini creating a image of the founding fathers of the United States as women and diverse races, which was historically inaccurate.
However, AI also increases cybersecurity risks when it makes false assumptions. A biased machine could view legitimate activities as threats and disrupt usage. The system can produce false negatives and eventually adapt and allow bad players to enter. AI does not hesitate to target groups of users and monitor them more closely than others, because AI is built by people.
Although AI is a valuable tool for monitoring cyberattacks, users should be aware of its limitations, and human workers should monitor it to avoid serious transgressions due to system bias.
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 security bias:
1. Use small language patterns
Large language model datasets rely on enormous amounts of information. It may be best to create several small sets of language models and deploy each to cover a single aspect of security.
As a result, you can use fewer resources and create AI programs that can perform more specialized tasks, such as focusing specifically on monitoring SQL injection attempts.
2. Focus on diversity
Although small language models work well, diverse data sets are crucial for larger or more comprehensive operations. Ultimately, a machine does not have the same ability to reason as a human. Although a statistic may show a trend, humans can use critical thinking to understand that it is not an indicator of every person of a gender or race.
3. Train staff
A study shows that a simple 10% of the global workforce has the AI skills needed for the future. Since humans are essential to successfully using AI without inherent biases, training staff on how to add language models to AI and work within the parameters of what is possible is essential to successfully integrate AI into cybersecurity.
Start with your IT team, as they will handle security tasks. However, all employees should eventually be trained to initiate the appropriate settings for machine learning. Give them the tools to know when to use AI and when to stop it altogether. Real-world examples, role-playing, and cybersecurity experience help.
4. Implement bias detection
Use bias detection tools to identify bias in the system. When AI focuses only 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 should be audited frequently and tested by humans to eliminate problems. Adding people to the process helps you locate and remove any biases before they become security holes that hackers can exploit.
5. Deploy advanced threat identification
Advanced detection allows businesses to respond quickly to cyberattacks and avoid data breaches. Attack simulations can train machines to better recognize malicious users and reduce incident response rates.
The better the AI understands the system’s uses and patterns, the quicker it will identify anything unusual. Even though the machine sometimes has false positives, human monitoring helps correct any errors and allows the program to better understand what constitutes a threat.
Harness the power of explainable AI networks so your IT team can understand how AI models make decisions about what constitutes a threat. The more you study how decisions are made, the better you will be able to identify biases and weaknesses and address them.
6. Diversify your data
Humans have biases, whether they want to admit it or not. Adding extreme caution to the elements you add to your training models can make a difference in the biases developed by the AI.
A team of researchers works best when they have different worldviews. Monitoring the data and ensuring there are no inherent biases already built into the information can make a difference in how the program works to protect your organization from hackers while keeping access available for employees and customers.
Adopting 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 need to have a diverse team that monitors each other and builds data sets over time.
Only with mutual respect and understanding can you create an AI program that avoids bias and works as intended to protect employees and stakeholders from the impact of a cybersecurity event.
