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CRECSOTechnologyArtificial Intelligence: A key to Outsmart Cyber Attackers & Fast To Track and Recover

Artificial Intelligence: A key to Outsmart Cyber Attackers & Fast To Track and Recover

AI-based cybersecurity solutions are becoming increasingly popular as they can quickly detect and respond to cyber threats, protecting organizations from potential attacks. With ML and advanced algorithms, AI can analyze large amounts of data to identify patterns and anomalies that human analysts would otherwise miss.

AI (Artificial Intelligence) can play a role in helping organizations prevent and recover from cyber-attacks.

AI algorithms can analyze large amounts of data and detect anomalies that may indicate a security breach.

For example, AI can identify suspicious network traffic, detect phishing emails, or find malware that has infiltrated a system.

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AI can automate responding to a cyber attack, freeing human security personnel to focus on more complex tasks.

For example, AI can be programmed to isolate infected systems, shut down specific network connections, or perform other actions to contain the attack.

However, it’s important to note that AI is not a silver bullet for cybersecurity, and it’s still necessary for organizations to implement a comprehensive security strategy that includes multiple layers of protection.

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AI algorithms must be trained and updated regularly to ensure they can effectively detect and respond to new attacks.

Between cyber security and cyber criminals, it’s a fact that the latter always have a head start. A new, smart, and rather effective strategy to catch up on the lag is Artificial Intelligence (AI).

Let’s find out how things work here! In the last few years, AI made a huge leap into many different domains beyond chatbots in customer service, industry automation, and the typical smartphone assistants like Apple’s Siri, Microsoft’s Cortana, etc.

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As we speak, AI today has integrated with cyber defense offering revolutionary cyber security solutions when it comes to identifying cyber-attacks and tactics practically in real-time.

It can also perform in-depth analysis and even fight back quicker than any other existing solution! In fact, AI-powered systems can optimize automatically and continuously, self-learn, and adapt to the ever-changing IT requisites concerning the current threats.

The real picture: Five malware a second

The everyday experience in almost any enterprise would make you realize the need for smart solutions. This is further fuelled by digitalization and stricter networking by the Internet of Things (IoT), which give hackers a platform to advance their assaults.

There isn’t a single day when we don’t see some 400,000 new malicious programs and web-based activities, adding to the overwhelming number of five per second. No wonder why IT security teams face a challenge in detecting the attacks and fights them back in time!

To make things more difficult, cyber-defense security offered by systems of yesteryear keeps shrinking.

Consider the scenario where the seed of the attack is implanted unnoticed, and more or less 150 days go by, during which attackers are free to come and go within the enterprise only to embed deeper into the core structure.

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AI ready for security

AI embedded in the security systems changes everything from deployment to breach detection and taking on-time countermeasures.

This is possible due to the dubbed “Machine Learning,”; a subset of artificial intelligence. You’ll be surprised to know the roots of this particular method dates back to the 1960s, but the advanced computers of today and exposure to technology present a perfect platform for deployment.

Within the domain of cyber-defense security, two methods of machine learning are most relevant;

Supervised machine learning

Supervised machine learning concerns the system being fed known data samples and hacker behavior. The system then abstracts the samples and common patterns and learns how to utilize the knowledge in the future.

Unsupervised machine learning

Unsupervised machine learning is where the system automatically adapts to the environment where it’s being deployed.

In a routine context, it copes with common aspects of data traffic within the enterprise network so that it’s able to detect even the slightest deviation without any pitfall. This defense method is further bolstered by combining two different methods.

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Deep Learning to dive much deeper

Machine learning in security systems is far more effective with “Deep Learning” technologies.

The methods mimic human cognition and thereby stimulate a tightly entwined network of nerve cells; it’s a real “neural network” deployed in a hierarchical pattern on different levels.

This way, it starts working from simple network dependencies and moving up to a more complex level.

Deep Learning has proved superior to even highly intelligent and experienced humans, as proved by the Deep Learning system programmed by Google.

Remember that epic moment when it defeated the world’s best players of “Go,”; a Chinese board game. The number of possibilities in the game is too broad for any computer to analyze, yet; a system programmed by Deep Learning ousted some of the best players.

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New threats: Identification & foresight

In a broad cyber-defense security world, Deep Learning can identify new threats, understand the behavior pattern of the attacker, and recognize new malware and tools being used.

It helps take timely action before a virus is triggered, saving critical IT infrastructure from total collapse and downtime.


Unless something broader and incredible, AI has it all to outsmart cyber attackers!

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