4 Unexpected Ways AI Bias Can Jeopardize Cybersecurity

Bias in artificial intelligence (AI) is one of the technology’s most prominent issues. Stories of AI repeating and even exaggerating human prejudices are common in cautionary tales about the overuse of AI in sectors like HR and finance. Still, many companies overlook the risks AI bias poses in cybersecurity.

AI can be a game-changing tool in threat detection and user authentication. Most security misgivings about it, though, revolve around its data dependency or surveillance issues. These are legitimate concerns and deserve attention, but it’s important not to overlook the impact of bias, either. To illustrate why, here are four ways AI bias can jeopardize cybersecurity.

1. Overlooking Security Threat Sources

False security assumptions are the primary security concern over bias in artificial intelligence. Human prejudices, however subtle, in training data, can lead AI models to draw incorrect conclusions about what constitutes a risk. This oversight can lead these models to overlook some threat sources.

Imagine an AI developer who believed foreign hackers and hostile nation-states posed the most threats to U.S.-based companies’ security. That bias could seep into the training data and model development, causing the AI to focus on traffic from nations like Russia or China. However, the U.S. accounts for less than 2% fewer cyberattacks than China, so this bias could drive the AI to overlook the considerable threat of domestic cyberattacks.

The difficulty here is that developers may not even realize they have these prejudices. It can also be hard to see how training data may mislead the model into developing these biases.

2. Limited Threat Detection Scopes

Similarly, AI bias can limit the range of threats a security bot looks for and protects against. The word “bias” stirs up images of racism, sexism, and ethnocentricity, but prejudices can also cover less dramatic false assumptions. It could be an issue of incorrectly believing some attack vectors are more prevalent than others, which limits AI defenses.

In these cases, bias causes AI to focus on specific symptoms of a potential breach instead of taking a more holistic approach. The model may then be effective against the most prominent threat types but fail to recognize other attack vectors.

This limited threat detection scope is dangerous because cybercrime is dynamic. Cybercriminals continually adapt their approach, and over 1,600 new malware variants emerge daily. An AI model only targeting specific, known attacks will quickly fall short.

3. Generating False Positives

Bias in artificial intelligence can also jeopardize security through false positives. This threat is easy to overlook because many organizations have successfully reduced false positives by implementing AI detection tools, but training biases raise the risk of over-classification.

AI developers could associate abbreviations and internet slang with phishing, leading the model to classify all emails containing this language as spam. As a result, casual communication between employees would trigger phishing warnings.

At first, it may seem beneficial to err on the side of caution, but these false positives take attention and resources away from real threats. Many organizations face strained security teams and tools amid rising cybercrime and IT talent gaps, making those distractions more impactful.

4. Misleading Security Insights

Human biases in AI can also affect applications outside of real-time monitoring and reporting. Many businesses also use AI to perform security audits or analyze historical data to reveal larger trends, and bias can affect these use cases, too.

The same prejudices that cause AI monitoring to overlook some risks and over-focus on others can lead to audit oversight. Imagine the developers building an AI security model trained it mostly on data about external attacks. The model may then tell an organization with strong external controls it’s entirely safe despite the company’s minimal protections against insider threats.

Misleading security insights like this become increasingly dangerous the more companies rely on AI analytics. Rising trust in AI can then lead to a false sense of safety, with this complacency eventually leading to damage from preventable cyberattacks.

How to Stop AI Bias From Threatening Security

The risks of AI bias are already pressing but will only grow from here. AI experts now predict artificial general intelligence — more versatile, powerful AI that rivals human intelligence — is just three to seven years away. Organizations must learn to prevent and manage bias in AI before then.

Understand Where AI Bias Comes From

The first step in minimizing bias in artificial intelligence is recognizing how it seeps into these models. In many cases, these prejudices come from training data. If this data doesn’t entirely reflect reality or contains long-held, historical human biases, it will produce a biased AI model.

Subtle implicit biases from developers are another common source. Data poisoning attacks, which have targeted Google AI systems at least twice, can intentionally produce AI bias, too.

When businesses understand these sources, they can take preventative measures. These protections include stronger access and authentication controls around AI training databases, auditing training data for bias, and coding AI models to actively dismiss known human prejudices.

Emphasize Diversity in Development

It’s also important to ensure diversity among AI dev and deployment teams. If every employee building or training an AI model comes from the same background, they’ll likely share similar biases, making it difficult to catch these issues. By contrast, a diverse team is more likely to produce a fairer, more holistic approach to data analysis.

This diversity applies to social factors like race, gender, and socioeconomic background as well as professional ones like employees’ specialty within the company. The more diversity in experiences and ideas a team has, the more likely they are to catch and correct biases in AI.

Some companies may lack this diversity within their AI-skilled workforce. In those cases, turning to outside experts or bringing in temporary talent may be necessary.

Monitor for Bias Before and After Deployment

Development and security teams should also be proactive about looking for bias in their AI solutions. These prejudices can be subtle, so they may not naturally rise to the surface until it’s too late. Active, thorough auditing is necessary to stop them before they cause any damage.

This review must happen during development and training, before implementing a finished model, and regularly after deployment. Bias is often subtle, and many AI models use black-box approaches that hide how they come to their decisions. Consequently, some signs of this issue may not emerge immediately, so ongoing review is essential.

Beware of Bias in Artificial Intelligence

Bias in artificial intelligence may seem chiefly a social issue, but it has a considerable impact on security as well. As AI in cybersecurity grows, organizations must remember these risks and take action against prejudice in their intelligent systems. Failure to do so could result in severe vulnerabilities.