Even if we are not always aware of it, artificial intelligence is now everywhere around us. We are already accustomed to personalized recommendation systems in e-commerce, customer service chatbots powered by conversational AI and much more. In information security, we’ve relied on AI-based spam filters for years to protect us from malicious emails.
These are all well-established use cases. However, since the meteoric rise of Generative AI In recent years, machines have become capable of much more. From threat detection to incident response automation to test employee awareness through simulations phishing emails, the opportunity for AI in cybersecurity is indisputable.
But with every new opportunity comes new risks. Threat actors are now using AI to launch ever more convincing phishing attacks on a scale that was not possible before. To anticipate threats, those on the defensive lines must also AIbut its use must be transparent and ethically driven to avoid falling into the realm of gray hat tactics.
Now is the time to information security leaders to adopt responsible AI strategies.
Balancing Privacy and Security in AI-Driven Security Tools
Crime is a human problem, and cybercrime is no different. Technology, including generative AI, is just another tool in an attacker’s arsenal. Legitimate companies train their AI models on vast swathes of data scraped from the internet. Not only are these models often trained through the creative efforts of millions of real people, but they also risk harvesting personal information that has fallen into the public domain, whether intentionally or not. As a result, some of the largest developers of AI models now face lawsuits, while the industry as a whole faces increasing scrutiny from regulators.
Even if threat actors care little about the ethics of AI, it’s easy for legitimate companies to unwittingly end up doing the same thing. Web-scraping tools, for example, can be used to collect training data to create a model to detect phishing content. However, these tools may make no distinction between personal and anonymized information, particularly in the case of image content. Open source datasets like LAION for images or The Pile for text have a similar problem. For example, in 2022, a California artist discovered that private medical photos taken by her doctor had ended up in the LAION-5B dataset used to train the popular open source image synthesizer Stable Diffusion.
There is no denying that reckless development of verticalized AI models in cybersecurity can result in greater risks than not using AI at all. To prevent this from happening, security solution developers must maintain the highest standards of data quality and privacy, especially when it comes to anonymizing or protecting confidential information. Laws such as Europe’s General Data Protection Regulation (GDPR) and the California Consumer Privacy Act (CCPA), although developed before the rise of generative AI, provide valuable guidelines to inform ethical strategies of AI.
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Emphasis on privacy
Companies use machine learning to detect security threats and vulnerabilities long before the rise of generative AI. Systems powered by natural language processing (NLP)behavioral and sentiment analysis and deep learning are all well established in these use cases. But they also pose ethical problems in which privacy and security can become competing disciplines.
For example, consider a company that uses AI to monitor employee browsing histories to detect insider threats. While this improves security, it may also involve capturing personal browsing information – such as medical research or financial transactions – that employees expect to be kept private.
Privacy is also a concern when it comes to physical security. For example, AI-based fingerprint recognition can prevent unauthorized access to sensitive sites or devices, but it also involves the collection of highly sensitive biometric data which, if compromised, could cause problems lasting for the people concerned. After all, if your fingerprint data gets hacked, you can’t exactly get a new finger. This is why it is imperative that biometric systems are kept under maximum security and supported by responsible data retention policies.
Keep humans informed of responsibility in decision-making
Perhaps the most important thing to remember about AI is that, just like humans, it can make mistakes in different ways. One of the central tasks of adopting an ethical AI strategy is TEVV, or test, evaluation, validation and verification. This is particularly the case in an area as critical as cybersecurity.
Many AI risks manifest themselves during the development process. For example, training data must undergo a thorough TEVV for quality assurance, as well as to ensure that it has not been manipulated. This is vital as data poisoning is now one of the primary attack vectors deployed by the most sophisticated cybercriminals.
Another problem inherent to AI – just like people – is that of bias and fairness. For example, an AI tool used to flag malicious emails may target legitimate emails because they feature vernacular signs commonly associated with a specific cultural group. This results in unfair profiling and targeting of specific groups, raising concerns about unfair actions being taken.
The goal of AI is to augment human intelligence, not replace it. The machines cannot be held responsible in the event of a problem. It’s important to remember that AI does what humans train it to do. For this reason, AI inherits human biases and poor decision-making processes. The “black box” nature of many AI models can also make it notoriously difficult to identify the root causes of such problems, simply because end users have no idea how the AI makes the decisions they make. ‘she takes. These models lack the explainability essential to achieving transparency and accountability in AI-driven decision-making.
Keeping human interests at the heart of AI development
Whether developing or using AI – in cybersecurity or any other context – it is essential to keep humans informed throughout the process. Training data should be regularly audited by diverse and inclusive teams and refined to reduce bias and misinformation. Even though individuals themselves are prone to the same issues, ongoing supervision and the ability to explain how the AI draws its conclusions can greatly mitigate these risks.
On the other hand, simply viewing AI as a shortcut and human replacement inevitably results in AI evolving in its own way, being trained on its own outcomes to the point that it only amplifies its own shortcomings – a concept known as AI drift.
The human role in protecting AI and being responsible for its adoption and use cannot be underestimated. That’s why, instead of focusing on AI as a way to downsize and save money, companies should invest all their savings into reskilling and transitioning their teams to new, adjacent roles. to AI. This means that all information security professionals must prioritize the ethical use of AI (and therefore people).
