Technology has evolved significantly in recent years, largely due to technological advances. AI. Around 51% of businesses now use it to cyber security and fraud management because it can detect a potential breach before it causes problems.
However, the effectiveness of AI may make people too dependent on this technology. Total confidence in artificial intelligence led 77% of these companies to discover a flaw in their AI system. As technology continually evolves, these issues are likely to recur, especially if left unchecked.
That said, this doesn’t mean businesses should avoid using AI for their cybersecurity needs. There is no doubt that AI can be an asset when used correctly. Companies should instead use it to augment human intelligence rather than replacing it. Without including human contribution and monitoring of the security formula, the chances of creating a blind spot can be very high.
Marketing Director at Kualitatem.
The question of AI bias in cybersecurity
AI systems can easily detect a threat within seconds because they can process the data present extremely quickly. The main problem is that training the AI takes a long time – sometimes it can take months for the AI system to fully understand a new procedure. For example, ChatGPT draws most of its data from information before 2023. The same goes for most AI tools, as they require constant training and updating to keep their information accurate.
With increasing cybersecurity threats, this can be very problematic. As hackers create new and different ways to breach security systems, AI technology that is not up to date risks not knowing how to react or missing a threat altogether.
In this scenario, human involvement is necessary because people can use their intuition and experience to determine whether or not there is a real threat to cybersecurity. Without this human-centric skill, relying entirely on AI can generate false positives and negatives, which can lead to damaging cybersecurity breaches or wasted company resources.
Cultural biases can impact training data
Some AI systems can be trained with new data every week, ensuring that they stay up to date most of the time. However, no matter how diligent trainers may be, there is always a risk of cultural bias in the data trained. For example, the United States has been at the forefront of some of the latest advances in cybersecurity. The problem is that this was done entirely in English so that AI systems could miss threats from non-English speaking regions.
To avoid this problem, it is essential to test systems and programs that use a culturally diverse set of AI tools and human participation to cover potential blind spots.
The challenges of algorithmic bias
AI systems rely on the data they have been trained with to take a particular action. If this data is incomplete or the algorithm has flaws, there is a high chance that it will lead to false positives and negatives, as well as inaccuracies.
When this happens, the AI may begin to hallucinate, presenting courses of action and prompts that seem logical but are ethically incorrect. If the system were configured to follow the direction of the AI without human intervention, the consequences could be major and time-consuming for the company.
These hallucinations can occur at any time, but they can also be avoided. For example, humans can organize and validate information regularly, ensuring that everything is complete and up to date. The human side can also provide intuition, identifying potential biases that might otherwise compromise the security algorithm.
AI is generally only as good as the information it is given, which is why it requires constant monitoring. If left alone for long periods of time, the algorithm can become outdated and make decisions that are no longer current. The human brain should provide the innovative aspect that AI often seems to lack.
The question of cognitive biases in AI tuning
AI systems leverage complex details from a vast pool of data to make an informed, non-emotional decision. However, the problem is that the data provided to the AI also comes from humans. Besides their knowledge, AI can also absorb their potential biases or imitate their lack of knowledge. Ultimately, AI systems are like a sponge: if the data trainer has biases, there is a good chance that the artificial mind does too.
For example, let’s say you create a security program preventing cybercriminals from accessing your account. database. However, you have no knowledge in the field of cybersecurity; you just have the computer skills needed to build a good algorithm. This lack of knowledge could be reflected in the algorithm written to predict how a cybercriminal might attack.
A diverse team is generally recommended to prevent this from happening. Not only can they supplement the data pool, but they can also detect certain evasion techniques that could otherwise bypass the AI system. This could significantly reduce potential breaches and protect its system from a hidden threat.
The essential
Ultimately, while AI systems can be a significant asset in reducing cybersecurity workload, they cannot operate independently. To ensure businesses are fully protected against cybersecurity threats, AI must be used to augment human intelligence rather than replacing it. In this way, costly and time-consuming errors can be avoided in the long term.
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