Artificial Intelligence has already widely penetrated cybersecurity across many industries. The majority of businesses already believe that they wouldn’t be able to detect and withstand attacks if not for AI assistance. According to the Capgemini survey, more than 80% of telecommunication businesses use AI for security, in banking 75% executives reported adopting the technology – and a similar tendency is obvious for all major industries.
Artificial Intelligence is clearly taking over day-to-day security issues, but it proved its capacities in solving complex problems as well. And not just in protection, but in hacking, too. Let’s take a look at how AI is used by both sides and what lessons corporations can learn from examining AI use cases.
Previous experience of using AI for cybersecurity
The main motivation for adopting AI for cybersecurity is an increasing complexity of IT threats. Before, businesses had to face “typical threats” like Zeus trojans, but as we learned to handle those, new species emerged. Recently, we faced Ryuk ransomware, smart botnets, and an evolved trojan, Triickbot.
Evolved threats, however, aren’t even the highlights of the risks. We need to understand that cybercriminals are just as enthusiastic about benefiting from AI and Machine Learning. Only smart protection methods can counter equally smart threats – and these are already around the corner.
Cases of AI use in cybercrime
There haven’t been many big AI-based attacks recently, but the talks about developing powerful AI threats are around for a while. The complexity of building an AI virus is in getting the infrastructure, necessary to maintain such a threat. For now, viruses were mostly small tools with a limited purpose that didn’t need AI, ML, facial recognition software, or big data.
AI threats for security
- Random changes of executable code: we’ve already seen polymorphic viruses that could do it, but AI can take the number of supported variables to the next level;
- Adapting to operating systems: the AI-based virus could find a smart approach to Kerner level functions or apply rootkits to avoid being detected;
- Recognizing the antivirus and attacking: a virus, equipped with an AI can detect anti-virus software and develop methods for attacking its code;
- Social detection: viruses could use conversational programming and facial recognition software to mimic human language and trick users into sending confidential materials, giving up access data, or simply perform cyberbullying;
- Creating updates: as soon as the previous version is detected by an antivirus, the virus could push a new version and keep at its malicious activities.
It’s likely that the next generation of viruses will be equipped with some forms of cyber technology. For now, we didn’t see much such threats due to financial and technological limitations: viruses are often developed by individuals who have no access to powerful infrastructure that would let them build AI. However, the technology quickly becomes more accessible, individual hackers will be able to implement it in their threats.
The Biggest Challenges In Cybersecurity
The active use of Artificial Intelligence and machine learning is not the only challenge that the organization and cybersecurity professionals need to face. There are others, caused by shortcomings in the current approach to security.
- Distant infrastructure. Today, systems communicate across continents, sending sensitive data al over the world. These transfers don’t undergo sufficient protection and are easier to break into.
- Manual detection. Human teams don’t have 24/7 focus on security threats and suspicious patterns. Most of the time, systems go unmonitored.
- Reactivity of security teams. Most security experts focus on facing threats rather than predicting them.
- Dynamic treats. Hackers have many strategies for hiding their locations, IPs, identities, and methods. The cybersecurity field, on the other hand, is a lot more transparent and open for research – data, created by businesses, is easily accessible by criminals.
Current role of AI and Machine Learning in Cybersecurity
Modern powerful viruses and future-generation AI-based viruses require a new approach to cybersecurity. It’s obvious that organizations need to be one step ahead of threats. Even if cybercriminals didn’t attack us actively with AI-based threats yet, it doesn’t mean it won’t happen soon. This is why businesses need to adopt Artificial Intelligence in their security for several reasons.
Reasons to adopt Artificial Intelligence in cybersecurity
- Fighting existing threats which find new approaches to getting into personal devices and corporate databases;
- Preparing in advance to face more powerful attacks, powered by AI and Machine Learning.
Examples of application of Artificial Intelligence and Machine Learning to security
Artificial Intelligence and Machine Learning don’t act only against known types of viruses – they can extend their impact to unknown types. The system can check a threat in the existing database, detect similar patterns with smart algorithms, and create a counterattack strategy. With time, such a system gets better at detecting and facing unknown threats, maximizing its speed and efficiency.
For instance, a common strategy of cybercriminals is hiding the malicious code among bundles of meaningless combinations that make it virtually impossible for a typical antivirus to find dangerous elements. OceanLotus, one of the Vietnamese hacking businesses, used this strategy and was detected by ML-based security software.
Other Artificial Intelligence possibilities in security
- Detecting viruses: Artificial Intelligence can analyze terabytes of data in the incredibly short period of time; quickly uncovering suspicious code fragments;
- Creating a virus database: Artificial Intelligence will store this information, process it, and learn from previously detected threats;
- Predicting the moves of cybercriminals: AI solutions can analyze existing threats, security news, and tendencies to forecast possible developments.
- Optimizing the functionality: AI can create smart insights that will help businesses improve their software and lower the chances of the next attacks.
Advantages of using Artificial Intelligence for cybersecurity
Artificial Intelligence fits well into the bigger picture of technological trends and advancements. The technology faces most of the cybersecurity challenges. It works continuously, providing constant oversight, can detect distributed threats, and learn on each new attack. Unlike standard solutions, it can find a way to deal even with an unknown agent, not only with exiting and cataloged threats.
Let’s recap the main advantages of using AI and machine learning in security that made AI a leader in a current global security market.
- Fast detection: Aritifcla Intelligence analytical and monitoring capacities far exceed human beings. unlike typical processing methods, AI has a smart insight: the technology is capable of facing unknown threats and building response strategies from scratch.
- No human errors: Artificial Intelligence can be used not only for small day-to-day clean-ups but for making important strategic decisions, as well. When businesses don’t check their decisions with the smart, data-driven algorithm they risk overlooking an important piece of data or missing a hidden pattern.
- Quick response: AI operates in seconds, quickly going through terabytes of data. With AI and ML security solutions, even big corporations are able to detect threats in seconds
- Automating routine work: Artificial Intelligence solutions saves time for the security team to focus on visionary and strategic objectives. Human experts are still a lot more superior in terms of creativity, and strategic insight, so it makes sense to free them from easily automized tasks, and let focus on the most important processes.
- A smart approach to education: Artificial Intelligence can be used to accumulate and generalize information about the current viral threats, create smart databases, classify risks, and respond.
Disadvantages of using Artificial Intelligence for cybersecurity
Even though AI indeed answers most of the cybersecurity challenges, it’s not an ultimate fix yet. Right now, Artificial Intelligence is still difficult to implement on a small scale and the technology itself requires improvements. In our opinion, ups far outweigh the downs, still, it doesn’t negate the fact that there are several drawbacks of AI solutions, compared to human beings.
- Cybercriminals are AI-savvy too: AI solutions for security can be used by hackers, too. The practices of cybersecurity teams are a lot more open than those of cybercriminals. We are unlikely to benefit from hackers’ experience, whereas they could potentially tap into organizations’ progress and reverse their findings to create a better threat.
- Cyberthreats evolve: even if you introduce AI to your business, it doesn’t mean you automatically become immune to all threats. Viruses and malware improves all the time, and even AI systems will need constant redesign, improvement, and maintenance.
- High adoption barrier: Aritifcual Inbtellignece still requires al lot of human resources and computing power, compared to typical antiviruses. You can simply install a ready software rather than spend time and money on building a custom AI solution. The good news, however, is that AI becoming increasingly more available and even small businesses can afford to build a security neural network.
Final Thoughts: Are you using Artificial Intelligence in cybersecurity?
Statistics, issued by Europoll and E3, remind us that we might be facing arms race. Organizations predict that hackers will start actively using AI in the near future, and lets’ face it, typical tools can’t accommodate such risks. As it is, most organizations aren’t ready to face highly intelligent viruses, malware, ransomware, and other forms of cyberthreats.
One thing is certain, adopting AI solutions can already help businesses spend less time and effort on their daily security tasks, while also keeping them better prepared for new risks. IT’s both the weapon against current threats and investment in the future. The technology is getting more available which means, soon no business will have a reason to delay adopting aI. Instead of waiting for custom tools to implement AI on a large level, it’s better to be ahead of the situation and start building powerful custom AI security too.