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Machine learning: How does it help cybersecurity professionals?

No threat facing the United States is as rapidly growing and challenging as the danger of cyberattacks. While cyberspace is integral to the country’s economy and defense, many private and public organizations still struggle to secure their systems. 

Adding to the difficulty of addressing this issue is the fact that online criminals have increased the frequency of their malicious activities. According to a recent survey, roughly 77% of organizations do not have an incident response team, while another 54% revealed that their IT departments are incapable of managing advanced cyberattacks. This is because cybercrime has become a sophisticated enterprise with hacking tools built on the latest technologies, such as artificial intelligence, available on the black market. 

While online criminals already use AI to penetrate organizational data and networks, there is reason to be optimistic. When cybersecurity professionals spend less time chasing false positives and concentrate more on higher-risk issues, security teams can become more efficient and productive. Although security experts can manage threats, they need more practical analytical power given the significant number of security events they face. 

However, it is worth noting that AI and machine learning alone are not a silver bullet. Businesses will always need to support technology with knowledgeable humans. This makes earning a cybersecurity online masters degree at a leading academic institution such as St. Bonaventure University highly useful and relevant in the modern business world, especially when it comes to decision-making, strategy and AI management. 

While working toward a cybersecurity degree, you will gain hands-on data experience and industry-specific skills that will help you fast-track career advancement. This is especially important when you consider the fact that the ongoing cyber skills gap affects organizations as well as the entire digital economy.

Cybersecurity strategy development

Traditional cybersecurity strategies often face challenges when managing large volumes of data and maintaining efficient operations in complex threat environments. As the industry deals with the evolving threat landscape, organizations can no longer simply set up a firewall or install an antivirus program and hope for the best. With attacks escalating, even the most safeguarded networks may still have vulnerabilities.

Cybersecurity professionals must adapt their strategies to meet dynamic requirements and close gaps that could otherwise impact productivity. Using machine learning, cybersecurity professionals can create a scalable cybersecurity strategy that enables them to better understand their threat environments and profiles so they can determine how their respective organizations stack up. This helps them to identify inadequacies and vulnerabilities so they can be addressed before hackers exploit them.

Scalable security

Although most organizations will acknowledge that no one is entirely immune to attacks, a scalable cybersecurity strategy helps them prepare in the event of an attack so they can react quickly and meet compliance standards. 

With machine learning algorithms, cybersecurity professionals can ensure they have the resources needed to isolate and neutralize threats, mitigate risks and maintain rigorous cybersecurity standards. This is especially important in the modern business world, where there is a pressing need for much greater accuracy when anticipating threats and opportunities.

The scalability and accuracy of machine learning allow for a coordinated response throughout multiple networks, systems and endpoints while ensuring threats are contained and mitigated effectively. With data and applications in many different locations, identifying trends across large volumes of devices is not humanly possible for cybersecurity professionals. 

Machine learning can do what humans cannot by enabling automation to provide insights at scale. This allows cybersecurity professionals to develop better strategies and improve overall security.

Instead of navigating conflicting access control lists for multiple devices and network segments, security professionals can classify complex situations and scenarios at scale to meet the challenge of cybersecurity now and in the future. A cybersecurity strategy aligned with a business’s current needs can predict potential challenges and help experts assess their risk.

Threat detection and response

Businesses face multiple security threats in today’s rapidly evolving online landscape, and they must implement comprehensive threat detection and response systems and processes to defend against these threats. However, implementing a threat detection and response plan is becoming increasingly complex given the rapidly evolving threat environment.

Online criminals are constantly leveraging new techniques, tactics and procedures to compromise networks. This means organizations must update their cybersecurity defenses regularly to stay ahead of these evolving threats. The continuous need for improvements in threat detection can place additional demands on an organization’s personnel and resources.

Recent estimates reveal that an organization faces up to 2,000 online threats daily. Even if you have highly qualified personnel, assessing these threats can take a toll on your cybersecurity team in the long run. Machine learning is a powerful tool because it can analyze significant amounts of data in seconds. This allows cybersecurity professionals to implement patches and remediate threats in real-time.

The advantage of machine learning is that it does not sit around and wait for threats. Instead, it proactively seeks out security risks that cybersecurity professionals might not know about by running a comprehensive systemic analysis of your network. This is crucial because many cybercriminals use AI and machine learning to get around cybersecurity.

Using machine learning ensures your cybersecurity teams can monitor every network interaction, reduce cloud security complexity, and eliminate unnecessary work. Given the ability of attackers to quickly penetrate an organization’s infrastructure, rapid detection and response are crucial for preventing unnecessary delays that could result in your data being exposed or getting locked out of your network.

Effective risk assessment

Cybersecurity involves several components, many of which can be easily overlooked. Machine learning is an excellent tool for cybersecurity professionals because it uses quantitative measures for practical risk assessment. This makes it easier for cybersecurity experts to assign scores to their respective network sections, which can help organizations prioritize resources.

Unlike traditional threat detection technologies, machine learning can assess previous and current datasets and identify which areas are primarily involved in attacks. This data can help quantify the likelihood and impact of an attack on a given network area while helping organizations reduce the risk of being victimized by further threats. Blind spots are relatively common in the cybersecurity industry as technology advances rapidly.

With help from machine learning, cybersecurity professionals can create risk models that process the data stored about previous breaches to learn from past mistakes and successes. Machine learning helps risk managers achieve complete risk visibility by combining the data required for standard risk assessment with unstructured information from third-party applications and reliable public sources.

This allows cybersecurity professionals to develop a comprehensive risk profile that is easy to understand and can aid in decision-making. However, it is worth noting that machine-learning-based cyber assessments do more than detect potential business threats. They can also help cybersecurity professionals simplify complex IT systems and processes. This allows them to quickly review their security controls and determine whether corrective or preventive measures must be enhanced or modified.  

Task automation

Modern cyberattacks are becoming heavily automated. If cyber professionals fight against these attacks manually, the battle becomes man versus machine, with highly unfavorable odds for the expert. For cybersecurity experts to successfully protect against automated attacks, they must fight fire with fire – or, in this case, a machine with a machine. Thanks to machine learning, cybersecurity professionals can level this uneven playing field using its automation capabilities for faster prevention.

Business owners and security teams manage daily tasks that never seem to stop, but the biggest issue is that most of these tasks are repetitive. Monitoring devices, completing penetration assessments and distributing security updates take time and energy. However, with machine learning’s automation capabilities, cybersecurity professionals no longer need to lose time conducting these mundane and repetitive tasks.

As a result, security professionals can focus on more pressing security issues and implement protections faster than attacks can progress. With machine learning acting as a force multiplier, these cybersecurity experts can scale responses to incoming alerts and prioritize resources effectively to address their organization’s critical vulnerabilities. Automating these tasks can also give cybersecurity professionals a head start in analyzing security anomalies.

Identifying emerging challenges

Although traditional signature-based threat detection systems are effective against known threats, they must be improved against emerging challenges. Cybercriminals can quickly penetrate signature-based detection systems by modifying the code or building innovative malware variants. In addition, these systems can generate a significant number of false positives, which can be a drain on resources.

This is the exact opposite of machine learning, which is trained using significant volumes of information – including network, endpoints and historical threat data – to identify complex patterns for cybersecurity professionals. When a security incident occurs, machine learning algorithms can assess the severity and impact of the incident. This makes it easier for cybersecurity professionals to react appropriately and prevent future breaches.

Becoming a cybersecurity expert today

With cybersecurity changing and evolving so quickly, setting up security measures once is no longer enough. As tools and techniques change, cybersecurity experts must adapt. Working toward an advanced cybersecurity degree can equip security experts with the skills and knowledge needed to stay ahead of emerging threats. It also helps them develop effective security solutions that are tailored to the current threat environment.

Here is a look at some of the academic areas you can specialize in when earning an advanced cybersecurity degree.

Data loss prevention

This specialization entails employing and maintaining security applications such as malware detection programs. While pursuing this training, you will acquire hands-on experience troubleshooting issues with innovative applications and maintaining server information. Depending on your chosen academic institution, you might also study user privacy issues in your graduate program.

Identity and access management

Specializing in identity and access management can prepare students to implement and enforce organizational policies effectively. Pursuing this specialty will expose you to methods for developing a system to authorize and grant permission to users based on their clearance levels.

Incident response and forensic analysis

Concentrating on incident response and forensic analysis courses can help students obtain real-world experience detecting and assessing various security breaches. They also cover the steps to take when cyberattacks occur. Some topics you might explore while working toward this specialty include malware detection and software sandboxing practices.

Architecture and policy

Specializing in architecture and policy will help you learn to design and implement secure network frameworks. This specialization is a good fit for someone who enjoys analyzing situations and creating multiple solutions to challenges. You will also explore developing functional diagrams, designing applications and studying various protocols.

Governance risk and compliance

Concentrating on this area can prepare you to identify and measure risks, perform internal audits, and build disaster recovery plans. This is a good specialty for someone with strong critical thinking skills and a knack for improving processes. While working toward this specialty, you will learn to develop suggestions that help organizations strengthen their security practices.

Zeroing in on a specific field allows cybersecurity professionals to improve their skills, demonstrate their expertise and find ways to improve their practice. This can improve their earning potential and help them stand out in the highly competitive cybersecurity job environment. With certifications and a master’s degree, cybersecurity professionals can take control of their career progression. 

As the threat landscape evolves, it is no longer enough for organizations to rely on human skills alone. Organizations can respond to and counteract these threats using the same technologies used to develop them to their advantage, particularly machine learning. 

Final thoughts

Using machine learning in various cybersecurity sectors can improve security processes while putting cybersecurity professionals in a position to succeed. This makes it easier for them to identify, prioritize, mitigate and remediate emerging threats.

Thanks to powerful innovations such as machine learning and AI, organizations can equip their cybersecurity professionals with the tools needed to stay ahead of threats and remain proactive in ensuring data and network security.

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