The Application Of Artificial Intelligence In Cybersecurity

The business attack surface is enormous and continually expanding and evolving. Depending on the size of your business, up to several hundred billion time-varying signals must be examined in order to quantify risk effectively.

Analyzing and strengthening cybersecurity posture is no longer a human-scale issue. In response to this unprecedented challenge, Artificial Intelligence (AI) based cybersecurity technologies have evolved to assist information security teams in quickly and effectively reducing breach risk and improving their security posture.

Due to their ability to quickly analyze millions of events and identify a wide range of threats, AI and machine learning (ML) have emerged as crucial technologies in information security. These threats range from malware that exploits zero-day vulnerabilities to risky behavior that could result in phishing attacks or the download of malicious code. These systems learn over time, relying on the past to detect new forms of threats in the present. Behavior histories provide profiles for people, assets, and networks, enabling AI to recognize and react to departures from established standards.

Data Analytics vs. Artificial Intelligence

Unfortunately, AI is now a highly popular and often abused term. Like big data, the cloud, IoT, and every other "next big thing," a growing number of businesses are seeking methods to get on board with AI. However, many of today's AI solutions fail the AI test. While they employ technologies that analyze data and allow the findings to drive certain outcomes, this is not AI; genuine AI is about duplicating cognitive capacities to automate jobs.

The key distinction is that AI systems are iterative and dynamic. They get smarter as they analyze more data, they "learn" from experience, and they become more competent and independent as they progress.

Data analytics (DA), on the other hand, is a static process that uses specialized tools and software to evaluate enormous data sets in order to derive conclusions about the information contained within them. DA is not an iterative or self-learning process.

Understanding The Fundamentals Of Artificial Intelligence

AI refers to technology that can comprehend, learn, and act on the basis of collected and generated data. Today, AI functions in three ways: 

  • Assisted intelligence, which is currently readily accessible, improves what people and organizations are already able to do.
  • Today's augmented intelligence allows individuals and organizations to perform things they couldn't achieve before.
  • Autonomous intelligence, which is being created for the future, includes devices that behave autonomously. When self-driving cars become widely available, this will be an example.

AI may be considered to have some human intelligence: a repository of domain-specific information; processes for acquiring new knowledge and mechanisms for applying that knowledge.

Today, AI technology includes machine learning, expert systems, neural networks, and deep learning.

  • Machine learning employs statistical approaches to enable computer systems to "learn" (e.g., incrementally increase performance) from data rather than being explicitly programmed. Machine learning works best when it is directed towards a particular goal rather than a broad objective.
  • Neural networks use a biologically inspired programming paradigm that allows computers to learn from observational data. Each node in a neural network adds a weight to its input that represents how right or erroneous it is in relation to the operation being done. The aggregate of such weights determines the final output.

Using AI In Cybersecurity

AI is well adapted to solve some of our most complex challenges, and cybersecurity is undoubtedly one of them. Machine learning and AI may be used to keep up with the bad guys," automating threat detection and responding more effectively than conventional software-driven techniques in today's ever-changing cyber-attacks and proliferation of gadgets. Simultaneously, cybersecurity brings certain challenges:

  • A massive attack surface.
  • Multiple devices per organization.
  • Hundreds of possible attack vectors.
  • Significant shortages of competent security experts.
  • Massive amounts of data have grown beyond the scope of a human-scale issue

Many of these issues should be addressed by a self-learning, AI-based cybersecurity posture management system. There are technologies available to correctly train a self-learning system to acquire data continually and autonomously from across your company's information systems.

As a consequence, new levels of intelligence are being sent to human teams in a variety of cybersecurity areas, including:

  • IT Asset Inventory entails compiling a thorough and accurate inventory of all devices, users, and applications that have access to information systems. Inventory also heavily relies on categorization and the assessment of business criticality.
  • Threat Exposure - Hackers, like everyone else, follow trends, thus what's popular among hackers changes on a regular basis.
  • AI-based cybersecurity solutions may give current knowledge about global and industry-specific threats to assist in making crucial prioritizing choices based not just on what might be used to attack your organization, but also on what is likely to be utilized to attack your enterprise.

Real-World Examples Of AI Applications In Cybersecurity

Machine learning can scan enormous volumes of data fast and interpret it statistically. Modern businesses create massive volumes of data, so it's no surprise that technology is such a powerful tool.

AI-assisted danger detection:    ED&F Man Holdings, a commodities dealer, was involved in a security issue some years ago. According to an independent audit, the company's cybersecurity procedures and technologies needed to be improved. Vectra picked Cognito, their AI-based threat detection and response platform. Cognito captures, saves, and enhances network information with unique security insights. It detects and prioritizes threats in real-time using this information and machine learning algorithms. Cognito assisted ED&F Man Holdings in detecting and blocking various man-in-the-middle assaults and ending an Asian crypto-mining operation. Cognito also discovered command-and-control malware that had been hidden for many years.

Security checks:    Immigration authorities and customs officers may discover persons lying about their intentions via security screening. However, the screening procedure is prone to errors. Furthermore, human-based screening might lead to mistakes since people are tired and easily distracted. The US Department of Homeland Security has created a technology called AVATAR that analyzes people's body motions and facial expressions. AVATAR uses AI and Big Data to detect subtle differences in facial expressions and body motions that may indicate suspicion.

Crime prevention & security:     The New York Police Department has used the Computer Statistics (CompStat) AI system since 1995. CompStat is an early AI that incorporates organizational management and philosophy but depends on various software tools. The technology was the first tool used for "predictive policing," Since then, numerous police stations around the United States have employed CompStat to investigate crimes. AI-based crime analysis programs, such as Armoury, located in California, use AI and game theory to forecast terrorist threats. The Coast Guard also uses Armoury for port security in Los Angeles, Boston, and New York.

Intelligent cyber attack detection:   The Energy Saving Trust is an organization that aims to cut carbon emissions in the United Kingdom by 80% by 2050. The organization was searching for a cutting-edge cyber security system to supplement its entire cyber defense plan. This involves protecting the company's vital assets from sophisticated cyber-attacks, such as intellectual property and sensitive client data. After giving it some thought, the company decided to focus on Darktrace's Enterprise Immune System. The Darktrace platform is built on machine learning technologies. The platform simulates the behaviors of every device, user, and network to understand particular patterns.

Darktrace automatically detects unusual activity and notifies the organization in real-time. Energy Saving Trust discovered multiple unusual behaviors as soon as they happened. It informed the security team to conduct further investigations, all while reducing any danger presented before significant harm was done.

Reduced Threat Response Time:   A worldwide bank was subjected to sophisticated cyber threats and sophisticated assaults. The bank's threat identification and response need to be improved. The previous approach required to be more capable of detecting and mitigating future generations of threats. The bank's security staff used Paladin's AI-based Managed Detection and Response Service (MDR). The threat-hunting service provided by Paladin is based on data science and machine learning skills. The bank's sophisticated assault detection and response capabilities have been improved. Data exfiltration, sophisticated targeted assaults, ransomware, malware, zero-day attacks, social engineering, and encrypted attacks are all examples of this.

Conclusion

AI has quickly emerged as a necessary tool for supplementing the work of human information security teams. Because humans can no longer scale to fully guard the dynamic business attack surface, AI delivers much-needed analysis and threat detection that cybersecurity professionals can act on to decrease breach risk and enhance security posture.

AI in security can identify and prioritize risk, detect malware on a network quickly, direct incident response, and detect attacks before they occur.

AI enables cybersecurity teams to establish strong human-machine collaborations that expand our knowledge, enhance our lives, and drive cybersecurity in ways that seem bigger than the sum of its parts.

Mike Sandru is a technology writer with  Suffescom Solutions

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