Cybersecurity management: AI and machine learning have a role to play

Cybersecurity management: AI and machine learning have a role to play

AI and machine learning are transforming the very nature of cybersecurity management. Companies in all sectors are implementing these tools to strengthen the protection of their IT systems. In a pandemic context where cybercrime is raging, today’s AI and ML features have the power to improve security.

For all businesses preparing their systems for digital transformation and all the threats that come with it, artificial intelligence and machine learning are worth gold. This is because these neighboring technologies make it possible to automate security and guarantee its effectiveness. For your business to capitalize on technological advancements, you will need to unleash the potential of new trends.

Here, we discuss the basics of artificial intelligence and machine learning applied to cybersecurity management. From authentication automation to cloud security coordination, here are the trends that will influence the industry from 2022.

Authentication Automation

Authentication is one of the key concepts of cybersecurity. Indeed, data integrity depends on the ability of a system to verify the identity of a user. This is where AI and, more specifically, machine learning (or “automatic learning”) come into play. Applied to the observation of patterns and the identification of threats in cybersecurity systems, they reinforce the protection of digital platforms.

Concretely, machine learning is a powerful risk assessment tool. By analyzing threats from third parties, modeling patterns from data, and identifying gaps in your defenses, these intelligent features ensure the integrity of your IT systems.

Automated application security tools use machine learning to identify incoming traffic and detect suspicious behavior and unauthorized access. To spot signs of danger, machine learning-based software relies on the data it has modeled and generated about cyberattacks. Then, some systems go so far as to neutralize the threat at the source. These days, such a level of response automation is essential to limit data compromises.

In just one year, thousands of major intrusions allowed unauthorized users to access millions of records. In the digital world, authentication has thus become a pillar of cybersecurity. This is where features like automated entry point identification and next-generation firewalls come into play.

Penetration testing is an integral part of any cybersecurity strategy. This includes exploring system access points to identify possible vulnerabilities. While human testing has its merit, automated testing based on machine learning can investigate different angles of attack faster and more efficiently than any manual process. As companies realize the benefits of automation, they will increasingly incorporate it into their cybersecurity management.

On a daily basis, we use AI for multi-factor authentication and other cybersecurity processes. These methods make it possible to guarantee the ownership of the data, in the same way as another technology that machine learning reinforces.

Blockchain strengthening

Currently, the blockchain drives the entire cybersecurity sector. And even the whole world of tech. These data systems were first designed and popularized by cryptocurrencies like bitcoin. However, the craze aroused by the blockchain now affects almost all areas, especially that of security.

The COVID-19 pandemic has led to an explosion of cybercrime in the face of which the cybersecurity industry has had to mobilize without delay. It is in this context that blockchain has experienced a resurgence in popularity as a means of data protection. Admittedly, blockchain is revolutionizing cybersecurity by embodying a next-generation first line of defense. But it cannot secure data on its own.

As proof, the theft of $72 million in bitcoin from Bitfinex, a Hong Kong-based cryptocurrency exchange. To achieve this, the attackers stole the authentication keys from the individual wallets of some users. They were thus able to access the accounts and seize the funds.

Since artificial intelligence and machine learning effectively protect systems against unauthorized access, the trend is to combine blockchain with AI-driven cybersecurity solutions. Since AI and ML applications can strengthen the integrity of a distributed ledger while improving the efficiency of data sharing paths, cybersecurity managers can expect a significant return on investment.

In addition, the use of AI to anonymize data stored in a blockchain for analysis and research purposes is a real asset, especially in sectors such as healthcare. In short, AI-based security solutions meet the imperatives of blockchain hardening. Thus, companies are spoiled for choice to improve the security of their data stored in a decentralized manner.

Cloud Security Coordination

Third and latest AI/ML trend: the coordination of cloud data network security using these smart tools. It helps to better protect businesses against data loss and theft.

More than a third of security managers believe that the rapid expansion of cloud systems is complicating security management. At the same time, 73% of companies have already suffered an incident due to immature security practices related to this increasingly large cloud infrastructure. In light of these challenges, more adaptive cloud security products have emerged to prevent and remediate data loss.

Suitable for businesses of all sizes, cloud solutions allow data to be stored in multiple locations. Thus, they protect this data against theft, attacks and ransomware. However, the size and complexity of these networks complicate their management. This is where AI and machine learning come into play.

Artificial intelligence allows security teams to seamlessly monitor their system while the algorithm scans telemetry data for possible threats. Without AI, it is impossible to ensure such large-scale surveillance. In this way, machine learning can assess the risks related to your security posture and suggest areas for improvement.

The future of cybersecurity management

No wonder AI and machine learning applied to cybersecurity management are gaining traction. When it is possible to analyze data and risks on a very large scale, data security reaches unprecedented levels. This will become all the more necessary as our society increases its dependence on virtual tools for work and communication. At the edge of the metaverse, it’s time to strengthen our cybersecurity practices.

In this area, the future of AI and machine learning will go through:

1. Automation of authentication

2. Strengthening the blockchain

3. Cloud security coordination

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Cybersecurity management: AI and machine learning have a role to play

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