The Role of Artificial Intelligence in Cyber Threat Forecasting & Remediation

cyber security testing

2021 was a record-breaker for the cybercrime industry. With an estimated loss of about 6 trillion dollars this year alone, it suffices to say that the following years are likely to bring more frequent and severe cyberattacks.

Despite spending millions of dollars every year on cyber security, the CIOs and CISOs of top companies find themselves in deep soup with regard to cyber threats. Without the safety net of cyber security testing, companies are doomed. Thus, being prepared can undoubtedly avoid some risks and hopefully safeguard these enterprises from most attacks.

The information technology landscape is evolving swiftly due to the boost in digital transformation post the pandemic. While businesses are being forced to move from brick & mortar to online platforms, data is moving from physical storage to cloud storage. However, this digitalization brings with it multiple security risks and safety challenges. Measures such as vulnerability assessments and regular penetration tests can help security managers circumvent these risks and challenges.

AI in cyber threat forecasting

Some organizations have ventured into applying Artificial intelligence concepts to their cyber security systems in recent years. They have successfully used it to spot threats and patterns in cyberattacks, forecast costly cyber threats before time, and be prepared to avoid, mitigate, and remediate these risks if and when the need arises.

If you are wondering how it is done, let’s break it down for you:

Artificial intelligence can help in spotting cyber threats and malicious activities early on. Traditional software systems fail to keep pace with newer types and sources of malware, therefore making AI a reliable source of protection.

Also, the use of sophisticated algorithms and machine learning insights can help detect ransomware. It can also aid in pattern recognition to identify the tried and tested ways of cyber attackers and detect even minute changes in their behavior, thus preventing any threats from entering the system.

Before we delve deeper, let us first understand the importance of artificial intelligence in cyber security in the present-day scenario.

The growing need for AI in cyber security

The increasing number and types of cyber threats and the alarming cybercrime statistics have made it impossible for even experienced cyber security professionals to detect or predict a potential cyberattack manually. In addition, the high volume of network logs makes it practically impossible to perform a manual analysis.

Intrusion detection and protection systems depend on AI to thwart network and host-based intrusions. Therefore, AI is fundamental to creating reliable IPSs. For example, in a signature-based detection system, cyber security experts detect malware by looking for specific AI-generated patterns. In other systems, anomaly-based detection (a machine learning concept) is used to prevent attacks by building a model of trustworthy activity and comparing any new behavior against this benchmark.

In addition, the increased number of IoT devices, be it in residential, commercial, or industrial use, has given rise to more sophisticated forms of cyberattacks. These include various types of cyber security threats such as DDoS attacks, zero-day attacks, social engineering attacks, leaked passwords, compromised cloud security, etc.

Additionally, it is a mighty challenge to protect big data from cyberattacks. AI steps in as a helpful tool to offer real-time analysis and monitoring of data patterns. It also helps with providing an extensive analysis of historical data, thus spotting leakage areas and loopholes swiftly.

Artificial intelligence can help in spotting cyber threats and malicious activities early on.

The 5 major applications of AI in cyber threat forecasting and remediation

AI can be helpful to cyber security teams in various ways. It is, however, essential for every organization to map and identify the areas where AI should be deployed in cyber security. This, in turn, helps in adding value and establishing appropriate goals. Here are some of the major areas where AI can help with cyber threat forecasting:

Cyber threat susceptibility and risk assessment

AI-powered superior predictive intelligence uses NLP capabilities to collate smart data by combing through both historical and the latest articles, news, and case studies on evolving cyber threats. This kind of intelligence can alert against the presence of anomalies and present prevention strategies.

After making a cluster set of common risks, cyber security experts can choose an appropriate remedial treatment.

Digital forensics

Digital forensics searches for traces of file modifications and any information violation. Digital forensics uses AI to delve deep inside computers and other smart devices to look for evidence of criminal activity. It also identifies network vulnerabilities and subsequently develops ways to mitigate these vulnerabilities.

Spam filtering and phishing emails

Using natural language processing techniques, AI helps detect language components generally found in spam and unwanted promotional messages. It also uses other ML-derived learnings to unveil sneaky phishing attempts and keep users safe.

Network monitoring

Open networks and reconnaissance spots are the number one exposed threat that attracts cybercriminals. AI-enabled threat intelligence uses identified data patterns to detect malware and ransomware attacks before entering the system. Thus, AI reduces incident response times and helps organizations comply with the best security practices.

Event correlation

Suppose an employee left the organization years ago, and now, someone is repeatedly using their login credentials or requesting a new password to access the system. What do we make of it?

In event correlation, AI flags off and alerts repeated suspicious behavior that would otherwise go unnoticed.

Summing it up:

Organizations worldwide are swiftly adopting digital platforms to scale their businesses and generate more income. However, the top priority of any ethical business will always remain its customers having a safe and secure experience. IoT proliferation and the increased usage of smart tech have opened a plethora of vulnerabilities and safety issues.

Cyber attackers are quickly pulling up their socks and devising newer, more harmful attacks. Therefore, quality assurance companies must stay one step ahead by leveraging AI to support cyber security measures. It is highly impossible to predict or tackle cyber security issues manually, especially when it needs to be done regularly. Artificial intelligence can help digital businesses operate safely with robust cyber security systems to protect their data and networks.

Needless to say, a good and dependable cyber security system needs to be tested repeatedly to measure its resilience against newer threats. In addition, regular cyber security testing can be a good proactive measure to prepare for cyber-attacks and remediate them as early as possible.

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About the Author: John Lucas