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  • Blog February 5, 2020

    AI in Cybersecurity: The Need of The Hour

    5 Minutes Read

Enterprises across the globe are being victimized by the growing threat of cyberattacks. According to a study conducted at the University of Maryland, there is a hacker attack every 39 seconds. This makes it necessary for you to hire a cyber security organization.

Due to the increasing number of cybersecurity breaches, enterprises are losing on millions of dollars. The report by Capgemini Research Institute suggests 20% of organizations incur loses of over 50 million US$ due to cybersecurity issues. It is worth noting that cyber-attacks are rising due to the latest digital technologies.

Realizing the importance of cybersecurity, organizations are increasing the pace at which artificial intelligence (AI) is adopted to ensure cybersecurity. Under AI, the use of machine learning (ML) and deep learning in cybersecurity is shooting upwards. This shows that more and more enterprises are employing AI.

In this blog, we talk about how organizations benefit from AI in cybersecurity. We also find out how they implement it for a higher business growth.

Benefits of AI in Cybersecurity

AI Reduces Detection Cost and Response to Breaches

As cyber security organizations use AI for cybersecurity, they can well understand and reuse threat patterns for new threat detection. The process of detecting incidents, investigating, and rectifying them becomes faster. Not only does time for detecting and rectifying threats is reduced, but also the cost is minimized.

There are organizations that have managed to accomplish quite higher cost reductions and the resulting benefits. Industry leaders opine that AI has a huge scope; it offers a plethora of opportunities for cybersecurity. AI-enabled cybersecurity is increasingly necessary for organizations. It allows you to transition from detection, manual reaction, and remediation towards an automated remediation.

PetSmart, a specialty retailer, based in the US, could save up to 12 million US$ by using AI in fraud detection. Having implemented an AI/ML technology, the legitimacy of each transaction was determined by comparing it against all other ongoing transactions. Fraudulent orders were thus identified and cancelled, thus saving cost and damage to the brand.

AI Speeds up the Rate of Responding to Breaches

It is critical to respond faster to secure your organization from cyber-attacks. Artificial intelligence reduces the overall time taken to detect threats and breaches by about 12%. You can also remediate a breach or execute patches faster. Reducing time metrics is key here.

Artificial intelligence even minimizes the dwell time; the period during which threat actors stay undetected. As you constantly scan data for known or unknown anomalies that show threat patterns, dwell time minimizes.

ZPower is a leading developer of rechargeable, silver-zinc batteries for microbattery applications. It uses artificial intelligence to identify and independently respond to emerging threats. As the battery manufacturer deployed the AI solution, the security team discovered that an employee had downloaded possibly a malicious software. The threat was removed and chances of any cyberattack were eliminated in real-time.

AI Leads to Higher Efficiency for Cyber Analysts

Artificial intelligence enables cyber analysts to spend more quality time in analyzing incidents that AI cybersecurity algorithms help to identify. Using AI is one of the top cyber security trends 2020.

One of the findings of the survey conducted by Capgemini Research Institute: 3 in 5 executives believe AI in cybersecurity hones the efficiency and accuracy of cyber analysts.

Through artificial intelligence and machine learning, cyber analysts can utilize good data to analyze the possible threats. Besides, talent is scarce in the field of cyber security. AI can play an instrumental role in bridging the demand-supply gap in this area.

AI Brings up New Revenue Streams

As cyber analysts grow more efficient due to artificial intelligence and machine learning, businesses end up generating higher revenue streams.

As smart products multiply, the attack surface for hackers increases. This, in turn, makes it necessary for manufacturers selling smart products to opt for cyber security services of artificial intelligence companies.

To illustrate this better, Digital Ghost technology by GE, provides an AI-enabled protective layer for industrial control systems. This technology leverages the digital twins to learn about the machine’s working pattern. It identifies if cyber-attacks are influencing the machine.

How to Implement a Roadmap to Implement AI in Cybersecurity

Create Data Platforms

The very initial essential step is to identify data sources and create data platforms. Remember AI in cybersecurity can succeed only when data sources connect to platforms. They should offer inputs for AI and machine learning algorithms. Availability of good quality data is key here.

Besides data identification, businesses must ensure that data is up-to-date and complete to achieve a high-quality output. You should conduct routine data quality checks. Only then can you be sure that data sets are updated and use them safely for AI algorithms.

Choose the Right Set of Use Cases

Use case selection is a constant process for AI implementation in cybersecurity. As you implement artificial intelligence, it takes time to run through the number of iterations needed to derive optimal actionable output. In this scenario, organizations should:

  • Start with the use cases providing significant benefits.
  • Focus on use cases as the data available here is complete, up-to-date, and refreshed often.
  • Make sure the subject-matter experts can verify the output from test use cases.

Collaborate Externally

It is crucial for businesses to collaborate with security professionals or threat researchers through crowd-sourced platforms. Enterprises can even create proprietary platforms to collaborate, discuss, and share the latest data threats.

Only then can you keep pace with the threats that other security professionals come across and enhance on threat intelligence.

After all, improving the logic of AI algorithms is detrimental to efficiently detecting threats. The best security teams are those that connect with their peers, either locally, or in their industry segment.

Deploy Security Orchestration, Automation & Response

Security orchestration, automation, and response (SOAR) technologies allow organizations to gather security data and alerts from varied sources. You can thus improve security management. It is a vital requirement to guarantee optimal output from AI in cybersecurity. These technologies enable businesses to conduct incident analysis and triage, benefitting from the integration of human and machine power.

Increased alert triage quality, improved security, effective operations center management, and less time to onboard cyber analysts, are a few of the advantages promised by SOAR.

Make Cyber Analysts AI-Ready

Upskill employees whose roles are subject to changes due to technology advances, like automation. At the same time, it is critical to draw on their knowledge of the company. Cyber security experts are of the view that instead of removing people from jobs, they should be trained around cyber. Cyber is not only about the technical, but also the knowledge of the company.

In addition to training your cyber analysts, you need to increase their efficiency to create proper interfaces. This way they can interact well with incident alerts and AI tools. Intelligence chatbots may help security operation centers to respond to high demand levels from people who need help.

Install Governance

It is a must for organizations to have a governance mechanism in place to ensure AI-enabled cybersecurity. The Security and Operations Center (SoC) should constantly:

  • Define roles and responsibilities for cyber analysts.
  • Supervise AI algorithm output by cyber analysts before an action is taken.
  • Create control processes to see if an AI algorithm does not behave normal.
  • Detect the risk tolerance for AI algorithm output generation.
  • Implement a mechanism to supervise output logic and upgrades of AI algorithms.
  • Constitute a back-up plan, in case AI algorithms are tempered with or fail.
  • Measure success of AI-enabled cyber security through key performance indicators.

The Bottom Line

Cyber security companies must identify where deploying AI in cybersecurity can deploy maximum value and chart out appropriate goals accordingly. Build a roadmap to address infrastructure challenges, application landscape, and data systems. Come up with best practices, skills, governance, and use case selection and implementation. Only then can companies prevent unnecessary losses and in fact, add additional sources of revenue.