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Cybersecurity: How AI Can Contribute To A Safer World?

Cybersecurity: Traditional security systems are based on comparing suspicious elements with threat databases, where known malware is gathered, including viruses, ransomware, and crypto jacks, among many others.

When any related program is detected, the tool acts, blocking and eliminating or isolating the suspicious item, preventing its dissemination. This procedure was the norm for many years and, at its peak, was responsible for high efficiency in containing digital threats.

However, the evolution of cybercriminals’ techniques and strategies has led to a change in the effectiveness of these solutions. Code changes, new features to make detection more complex, and adaptive behaviors of the latest malware have drastically reduced the effectiveness of traditional solutions. The same approach to criminals, based on several steps, with the insertion of codes for C&C actions, lateral movement and other tricks, also helps to overcome typical digital defenses.

Security teams, in response, began to adopt more isolated tools with dedicated functions that, in theory, would help to contain the new threats at different stages of the cybersecurity attack. More tools mean more protection options, but this has created another problem: too many alerts and prioritization issues. With more tools, the increase in unwanted redundancies, false positives, mismatches and too many unnecessary alerts often leads to an overload of teams. Continuously, the teams, unable to prioritize their attention and optimize their time, become less effective and, therefore, more space is created for the action of malicious agents.

A Smart Solution

It became clear that the solution could not simply be a linear increase in the number of tools and analysts to manage them; the new scenario called for a change in the fundamentals of cybersecurity solutions, another way of looking at and blocking threats. Thus, ​​using artificial intelligence (AI) in digital security emerged. 

Instead of being limited to comparing elements in databases, which leads to binary responses and gives more chance of evasion to malicious agents, AI allows adaptation of response to contexts and learning. Thus, the tool starts to analyze behaviors and scenarios, not just elements, and to learn new techniques based on past experiences. This raises the quality of the defense provided to unprecedented levels, allowing something hitherto impossible: to stop unknown threats.

As AI-based systems are not limited to information from threat databases (although they are still used and relevant), they can detect abnormal behavior on the network, servers and endpoints. Since today’s cyber attacks are carried out in several stages, malicious individuals on the web trying to gain unauthorized access to servers, folders, and environments become detectable during their lateral movement or sending data in C&C actions.

Tools developed with AI are more effective because they have a much more contextual action than traditional ones. By their nature, they can block advanced attacks such as file less and exploits, as well as identify and stop the spread of malware and ransomware infections, even if they do not appear in cybersecurity threat databases. 

Another highlight is the learning capacity of these solutions. Based on CSIRT databases, analysis of the environment, its programs and users, and the contained attacks, the tools become more innovative and solid, generating more effective protection over time.

The introduction of such a tool brings enormous benefits to the company:

  • Real-time protection: The visibility and rapid response of these products provide immediate protection even before a malware payload is installed, as well as against file less threats, C&C actions, and others;
  • Deeper analytics: With data collected and comparisons made by AI, analytics are much more accurate and relevant, generating more threat intelligence;
  • Blocking unknown malware: as it does not rely on a database of known threats to identify potential threats, these solutions can effectively contain even unknown and undisclosed threats;
  • Defense against discrete attacks: Malware that relies on native system tools and vulnerabilities tends to go undetected, but its behavior can be detected with AI.

MSS And AI, Together For More Security

Managed Security Services (MSS), having to deal with a plethora of data and needing to respond quickly and accurately to incidents in diverse contexts and customers, entirely depending on solutions like this to be effective. AI tools make its performance much more precise, with maximum prioritization of events, allowing assertive responses to each incident.

It is clear that the threat landscape is more complex and challenging, so the use of advanced resources is a priority for the leading MSS providers; with more intelligence, visibility and responsiveness, specialists can act with the speed and precision necessary to ensure the construction of safer environments for its customers and users.

Also Read: Cyber ​​Security – Top Threats And Applications

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