new trends and opportunities

The number of cybercrimes is constantly and rapidly increasing. So, over the past year, the losses of the Russian economy from the activities of hackers amounted to about 6 trillion rubles. According to experts, attackers are often not one, but several steps ahead of information security specialists and law enforcement officers.

According to the statistics of the Ministry of Internal Affairs, every fourth crime is now committed using IT technologies. For five years the number of cybercrimes in Russia has increased by 12.5 times. For seven years – and generally 20 times. Of course, something needs to be done about this, so the sphere of information security is also developing. Now artificial intelligence is coming to the rescue – there are several important directions for the development of the “AI + IB” symbiosis. Let’s talk about this today.

There are not so many important trends, but they can be called critically important for the entire industry.

Cyber ​​Threat Detection

Machine learning (ML) makes it possible to detect cyber threats in real time based on the analysis of data coming from different sources. Machine learning algorithms are constantly adapting and updating to detect threats to your system before attackers can identify vulnerabilities in the protection of the company’s network and implement threats. Algorithms “understand” all the nuances of the organization’s infrastructure and network, as well as possible attack scenarios.

Through training, data analysis, and understanding of behavior patterns, algorithms can detect anomalies in the system and prevent them before a full-fledged attack is carried out.

AI serves as a supportive but highly reliable tool for the cybersecurity professional. Over time, the direction of detecting cyber threats is gradually evolving, because, as mentioned above, cybercriminals also do not sit still.

Network security analysis

In many cases, network security is based on two main aspects:

Development of a security policy. It makes it possible to detect legal and not very network connections. If the company is not very large, then there are few problems with data analysis. But for medium and large companies, everything becomes more complicated, because it is difficult to create, manage and maintain a security policy for a huge infrastructure. Therefore, AI is becoming a reliable and effective tool for solving such problems. It helps businesses track and enforce rules and regulations and monitor complex networks for compliance. In the case of AI, all this happens much more efficiently than in the case of “manual” labor, i.e. involvement of specialists without AI tools.

Analysis of the network environment. A large number of companies do not have a sufficiently clear system for monitoring different workloads. The information security department therefore has to spend a lot of time and energy determining which group of workloads belongs to which applications. AI is able to optimize the performance of this task, here the machine copes better than a person.

Behavioral analysis

We are talking about monitoring user actions in real time. When anomalies are detected, the system is triggered using one of the protection scenarios. Any anomaly is marked as suspicious, so that the threat is identified with a high degree of probability.

The algorithms work with a wide variety of information, including employee hours (if someone logs in from an unfamiliar location, the activity is marked as suspicious), the geographic location used to log in to the network, device IDs, input patterns, etc.

In order for the algorithms to work effectively, the information security team needs to upload reference data into the system, against which reconciliation is carried out.

Cloud Infrastructure Security

And here AI comes to the rescue. The fact is that cybersecurity is more difficult to ensure as companies move to the cloud. Solutions designed for local area networks are not particularly good at this task.

But hybrid AI solutions, which provide the ability to track and analyze data in a variety of environments, work very well. More and more organizations are adopting a specialized approach where enterprise data is extracted from different architectures, compiled and parsed by a software platform. Hybrid systems are able to tirelessly monitor what is happening.

Fraud detection

Many users of banking services have experienced inconvenience when a bank tries to protect a client’s funds. Accounts are blocked, users cannot use the bank application while abroad – there are many such cases. This is because some organizations use a rather strict algorithm for detecting suspicious transactions. A little something wrong – and everything is blocked.

AI is more flexible. There are much fewer false positives when integrating artificial intelligence solutions. Moreover, AI quickly detects the source of the problem, so that law enforcement officers receive the necessary data in time.

Without additional, comprehensive solutions, it is extremely difficult for a business to counteract the coordinated actions of intruders. According to experts, in 2022, cybercriminals will begin to form new groups that will pose an even more significant threat than the “infrastructure” of attackers that is currently in place.

AI technologies will continue to develop in line with information security. Ultimately, artificial intelligence can make a real revolution in the world of cybersecurity – after quantitative changes turn into qualitative ones.

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