Introducing artificial intelligence into the cybersecurity field creates a vicious cycle. Cyber experts are now leveraging AI to power their tools and improve detection and protection capabilities, but cybercriminals are also leveraging AI for attacks. Security teams then use more AI in response to AI threats, threat actors ramp up their AI to keep up, and the cycle continues.
Despite its great potential, AI is severely limited when adopted in cybersecurity. AI security solutions have reliability issues, and the data models used to develop AI-powered security products always seem to be at risk. Additionally, AI often conflicts with human intelligence when implemented.
The double-edged nature of AI makes it a complex tool that organizations must better understand and use more carefully. In contrast, threat actors are using AI almost without restrictions.
lack of trust
One of the biggest challenges when implementing AI-driven solutions in cybersecurity is building trust. Many organizations are skeptical of AI-powered products from security companies. This is not surprising, as some of these AI security solutions are overhyped and underachieving. Many products touted as AI-enhanced do not live up to expectations.
One of the most touted benefits of these products is that they greatly simplify security tasks and allow non-security personnel to complete them. This claim is often disappointing, especially for organizations struggling with a shortage of cybersecurity talent. AI should be one of the solutions to the cybersecurity talent shortage, but companies that over-promise and under-deliver aren’t helping to solve the problem. In fact, it undermines the credibility of AI-related claims.
One of the main goals of cybersecurity is to make tools and systems easier to use, even for non-sophisticated users. Unfortunately, this is difficult to achieve given the evolving nature of threats and the variety of factors that can weaken your security posture (such as insider attacks). Almost all AI systems still require human direction, and AI cannot override human decisions. For example, AI-assisted SIEM can pinpoint anomalies for security personnel to assess. However, internal threat actors can prevent proper handling of security issues discovered by the system, making the use of AI virtually pointless in this case.
Nevertheless, some cybersecurity software vendors offer tools to maximize the benefits of AI. For example, AI-integrated Extended Detection and Response (XDR) systems have a strong track record of detecting and responding to complex attack sequences. XDR offers significant benefits that alleviate skepticism about AI security products by leveraging machine learning to scale up security operations and ensure more efficient detection and response processes over time.
Data model and security limitations
Another concern that undermines the effectiveness of using AI to counter AI-powered threats is the tendency of some organizations to focus on limited or unrepresentative data. Ideally, AI systems should be fed with real-world data to paint a picture of what’s happening in the field and the specific situations an organization encounters. But this is a huge effort. Collecting data from different locations around the world to represent every possible threat and attack scenario is extremely costly and even the largest companies try to avoid it whenever possible.
Security solution vendors competing in a crowded market are also trying to launch products as quickly as possible with all the bells and whistles they can offer, but with little or no consideration for data security. This may result in data manipulation or corruption.
Fortunately, there are many free, cost-effective resources available to address these concerns. Organizations can rely on free threat intelligence sources and trusted cybersecurity frameworks. Miter attack & CK. Additionally, AI can be trained on user or entity behavior to reflect behaviors and activities specific to a particular organization. This allows the system to go beyond general threat intelligence data (such as indicators of compromise or good or bad file characteristics) to examine details specific to your organization.
In terms of security, there are many solutions that can successfully thwart data breach attempts, but these tools alone are not enough. It is also important to have the right regulations, standards, and internal policies in place to comprehensively thwart data attacks aimed at preventing AI from properly identifying and blocking threats. The ongoing government-led AI regulatory consultation and MITER’s proposed AI security regulatory framework are steps in the right direction.
superiority of human intelligence
The time when AI can bypass human decisions is still decades or even centuries away. While this is generally a good thing, there is a dark side. While it’s good that humans can override AI judgments and decisions, it also means that threats targeting humans, such as social engineering attacks, remain powerful. For example, AI security systems automatically edit links in emails and web pages after detecting risks, but human users can also ignore or disable this mechanism.
In other words, the ultimate dependence on human intelligence hinders AI technology’s ability to counter AI-assisted cyberattacks. While threat actors automate the generation of new malware and propagation of attacks indiscriminately, existing AI security solutions areblack box problem“” of AI.
For now, the goal is not to have an AI cybersecurity system that works completely on its own. The vulnerabilities created by the dominance of human intelligence can be addressed through cybersecurity education. By holding regular cybersecurity training, organizations can ensure that employees practice security best practices and become more proficient at detecting threats and evaluating incidents.
At least for now, deferring to human intelligence is the right and necessary thing to do. Nevertheless, it is important to ensure that this does not become a vulnerability that cybercriminals can exploit.
Take-out
It is more difficult to build and protect things than to destroy them. Using AI to fight cyber threats has always been challenging due to a variety of factors, including the need to establish trust, the care required when using data for machine learning training, and the importance of human decision-making. Masu. Cybercriminals can easily ignore all these considerations, making it seem like they have the upper hand.
Still, this problem is not without a solution. Trust can be built through standards and regulations, as well as through a diligent effort by security providers to demonstrate a track record of delivering on their claims. Data models can be protected with advanced data security solutions. On the other hand, continued reliance on human decision-making can be resolved with sufficient cybersecurity education and training.
The cycle is still ongoing, but the silver lining is that the reverse is also true. This means that as AI threats continue to evolve, so too will AI cyber defenses.