Artificial intelligence helps in automating businesses. But unexpected AI bias can cause severe cybersecurity threats. This post explains how.
Ever since its inception, complex AI has been applied to a wide array of products, services, and business software. However, the algorithms that support these technologies are at a huge risk of bias. In fact, unexpected AI bias is one of the biggest issues faced by firms looking to deploy AI systems pan operations. That’s because bias can lead to costly business mistakes and undermine a brand’s reputation.
What’s more? AI is being increasingly deployed by businesses as a powerful tool to detect, predict, and respond to cybersecurity threats and data security breaches in real-time. In a survey report by Capgemini, 61 percent of businesses acknowledge that they will not be able to identify critical threats without AI. Naturally, biases in AI security models can create serious security issues for a firm.
Therefore, it’s critical to understand what AI bias is all about and how it can adversely affect your firm’s cybersecurity.
What is AI Bias?
Machine learning and deep learning models seem to be dissociated from human intervention; yet, let’s not forget that they are created by humans who are biased. Everyone has biases – conscious or unconscious prejudices that influence decisions. Therefore, these models and algorithms are prone to biases by their creators. Algorithms then learn from these biases and they quickly become the basis for unfair datasets and decisions. Hence, AI models, regardless of where they are applied, shouldn’t be biased.
Depending on where the algorithm is applied, these biases can affect various business operations. For instance, in a financial institution, AI bias can corrupt the ability of a system to conduct credit card fraud prediction. Further, it can negatively affect the way businesses manage their funds.
Source of AI Bias
Humans, of course! All algorithms and models are created by humans. Therefore, they reflect the biases of all those involved in creating these ML models, such as the designers, the data scientists, and others involved. AI models learn to make decisions based on the training data that comes with biased human decisions or portray historical or social discrimination related to gender, race, or sexual orientation.
For instance, Amazon’s hiring algorithm was selecting favored applicants based on words like ‘executed’ or ‘captured,’ words commonly used by men in their resumes. The eCommerce giant finally stopped using this AI recruiting tool to uphold diversity and fairness.
Though training data carries most of the blame for AI bias, the reality is more nuanced. Bias can creep in at any stage of the deep learning process, namely problem identification, data collection, and data preparation. Hence, fixing a bias in an AI-based algorithmic system is not easy.
Now, let’s see;
How AI bias can affect a firm’s cybersecurity efforts
Faulty Security Assumptions Can Threaten Your Firm’s Security
In the case of firms deploying AI for security, faulty security assumptions are often a result of unconscious biases in the model. Such biases can cause the system to qualify malicious internet traffic and miss out threats that can enter the firm’s network and wreak havoc.
For instance, a web developer may be biased towards an ally nation and allow all the network traffic from that country, considering it to be safe. Such biases can cause the algorithm to overlook a fraud element, a vulnerability, or a breach that may stem from that nation. This can pose a threat to the firm’s security.
Biased Training Data Can Lead to Dodgy Security Outcomes
A deep learning algorithm’s decision-making ability is only as effective and neutral as its training data. Training data is considered to be neutral until human prejudice is detected when it reaches the algorithm. Biased training data and flawed data sampling produce distorted results, causing businesses to make corrupt security decisions and outcomes.
For instance, if a spam classifier isn’t trained well enough on a representative set of benign emails, it is bound to produce corrupt results. So, if it is hit by emails with slang or other linguistic idiosyncrasies, it will produce false positives.
Tunnel Vision in AI Models Can Pose Serious Security Risk
The nature of cyberattacks varies across geographies and industries. If your firm’s AI model for cybersecurity isn’t trained to detect issues outside a particular setting, it will be unable to identify such threat patterns, your organization’s security can be easily compromised.
Since humans from a particular geography or industry domain train algorithms, these AI models often suffer from tunnel vision. In other words, they lead to bad AI security models that lack a 360-degree understanding of the cybersecurity landscape, the firm’s security posture, and the emerging threat patterns. Such models can be easily exploited by cybercriminals.
Hence, when training a security model, a firm should involve professionals from diverse backgrounds, geographies, and industry segments. This allows them to feed a variety of behavioral patterns and scenarios of security threats into the model and fill in the gaps in the threat detection process.
Over to You!
AI is being used by a majority of businesses to supercharge their cybersecurity. However, biases in ML or DL models can dampen these efforts and put your firm’s security at risk.
AI bias is entirely our responsibility. Biases can creep in at any stage of a machine learning process and negatively impact business operations, impact critical decisions, and encourage mistrust and discrimination. However, having a biased algorithm in the cybersecurity arena can cause serious issues.
Therefore, we should do everything within our capacity to tackle bias in AI security models. Here are a few tips to get you started.
- Set up processes to prevent the creation of biased algorithms. For instance, you can have the code reviewed by a third-party security expert. You can also hire an external developer to create a bias-detection algorithm like AI Fairness 360.
- Hire a diverse team of security professionals and developers to check for biases in the model.
- Make sure the training data is untouched and uncategorized. Also, if you are using third-party training data, check to see if the insights and patterns included are relevant to your business.
- The organization collecting and preparing the data should have a strong security posture and a comprehensive understanding of the threat landscape in your business niche.
AI, if used effectively can revolutionize a firm’s cybersecurity for the better. However, it’s critical to get rid of the biases that exist in various forms. Use the insights and tips shared in this post to identify and eliminate AI bias and boost your firm’s cybersecurity efforts.
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