Machine learning and AI are fundamentally transforming the nature of cybersecurity management. Across industries, businesses are applying these tools to better secure their information systems. Now, trending features of ML and AI stand to improve security outcomes in the cybercrime-riddled pandemic era.
For any business preparing its systems for the digital transformation and all the threats that come with it, ML and AI are invaluable. That’s because these related technologies allow for safety automation and efficiency that would be otherwise impossible. To seize the benefits of evolving tech for your ventures, you’ll need to make the most of modern trends.
Here, we explore the ABCs of machine learning and artificial intelligence in cybersecurity management. From automating authentication to coordinating cloud security, these are the trends that are shaping the industry in 2022 and beyond.
Automating authentication
One of the most important concepts in cybersecurity is that of authentication. The ability of a system to verify a user correctly shapes overall data integrity. Fortunately, AI and its subcategory of machine learning are being applied to pattern observation and threat identification in cybersecurity systems, making for safer digital platforms.
In fact, machine learning has proven to be a powerful tool in assessing risk. By analyzing threats from third parties, modeling patterns from data, and conducting penetration mapping, these smart features promote the integrity of information systems.
Automated application security tools use machine learning to identify incoming traffic, exploring it for misbehavior or unauthorized access. The machine learning software understands red flags based on the cyberattacks data it has modeled and developed in response to. Then, these systems can go so far as to shut down data breaches at their source. This is a level of automated response that is exactly what is needed to mitigate data breaches in the modern era.
In a single year, thousands of major breaches leave millions of records exposed to unauthorized users. Authentication in this digital landscape has become an integral aspect of cybersecurity as a result. This is where features like automated penetration mapping and next-gen firewalls come in.
Penetration testing is an integral part of any cybersecurity strategy. This involves the exploration of system access points to identify vulnerabilities. While human testing is great, automated tests with the power of machine learning can explore avenues of attack more quickly and efficiently than any manual process. Integrating this efficiency in cybersecurity management will increasingly become the trend in the business world as companies recognize the value of automation.
Daily, we use AI to enable multifactor authentication and other cybersecurity processes. These are methods of securing data ownership, which just so happens to be the goal of a related technology being boosted by machine learning.
Boosting blockchain
Blockchain is a big name in cybersecurity these days. In all the tech world, actually. These data systems were designed for and popularized by cryptocurrencies like Bitcoin. Now, however, blockchain’s appeal is recognized virtually everywhere, and security is a big part of that appeal.
The Covid-19 pandemic led to an explosion of cybercrime, one the cybersecurity industry has had to react to quickly. In the course of this work, blockchain has taken on new popularity as a means to protect data. Blockchain represents one of the biggest changes in cybersecurity as the next generation of frontline data protection. But blockchain isn’t perfectly secure on its own.
In one instance, $72 million of Bitcoin was stolen from Bitfinex, a crypto trading platform based in Hong Kong. How was this possible? Attackers stole the authentication keys for some users’ segregated wallets. From here, they were able to access the accounts and seize the crypto funds.
Since AI and machine learning are great at protecting systems from breaches of access, pairing blockchain with AI-driven cybersecurity solutions has become the trend in security. The potential of AI and ML applications for improving the integrity of a distributed ledger while increasing the efficiency of data sharing routes offers cybersecurity managers plenty of return on investment.
Additionally, the use of AI to de-identify data stored in a blockchain for research and analytics purposes has powerful implications, especially in industries like healthcare. With a layer of AI security, blockchain gets a much-needed boost. From here, businesses have multiple options for storing data more safely on a decentralized system.
Coordinating cloud security
This brings us to the final trend in ML and AI that we’ll cover in this article. These smart tools are being used to coordinate security among cloud data networks. As a result, businesses are better protected against data loss and theft.
More than a third of security decision-makers for companies said rapidly expanding cloud systems are making it difficult to manage security. Meanwhile, 73% of organizations say they’ve experienced a security incident as a result of immature security practices in this ballooning cloud infrastructure. In light of these challenges, more adaptive cloud security products are hitting the market, helping businesses prevent and recover from data loss.
Cloud solutions can work for any size of business, giving them the means to store data in multiple locations. As a result, data is kept safe from theft, attack, or ransomware. However, the size and complexity of these networks can make them difficult to manage.
That’s where AI and machine learning come in.
With the help of AI, security staff can maintain transparent oversight of a system as the algorithm explores telemetry data for evidence of a threat. The scale alone demands the help of artificial intelligence. From here, machine learning can produce risk assessments of security postures and make recommendations for improvement.
The future of cybersecurity management
It’s no wonder why AI and ML are being more and more popular in cybersecurity management. With the ability to parse data and risks at a superhuman scale, data becomes safer than ever. This will only become more necessary as society becomes increasingly reliant on virtual tools to communicate and work. With the metaverse on the horizon, now is the time to adopt safer cybersecurity practices.
The future of machine learning and AI in cybersecurity entails:
- Automating authentication
- Boosting blockchain
- Coordinating cloud security
Embrace these trends now for a system that just about secures itself.
