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AI in Cybersecurity: The Need of The Hour

POSTED ON: February 5, 2020 TAG: Digital Engineering

The growing threat of cyberattacks is victimizing enterprises across the globe. According to a study conducted at the University of Maryland, there is a hacker attack every 39 seconds1, making it necessary for you to hire a cybersecurity organization.

Due to the increasing number of cybersecurity breaches, enterprises are losing millions of dollars. The report by Capgemini Research Institute2 suggests 20% of organizations incur losses 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 usage shows that more and more enterprises are employing AI.

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

Benefits of AI in Cybersecurity

AI Reduces Detection Cost and Response to Breaches

As cybersecurity organizations use AI for cybersecurity, they can 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 fixing threats is reduced, but also the cost is minimized.

Some organizations have managed to accomplish relatively higher cost reductions and the resulting benefits. Industry leaders opine that AI has a vast 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 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, each transaction’s legitimacy was determined by comparing it against all other ongoing transactions. Fraudulent orders were thus identified and canceled, 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 critical 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.

Let us take an example here to understand the above point better. 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 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 time analyzing incidents that AI cybersecurity algorithms help identify. Using AI is one of the top cybersecurity trends in 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 cybersecurity. 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 generate higher revenue streams.

The attack surface for hackers increases when smart products multiply, making it necessary for manufacturers selling smart products to opt for cybersecurity services of artificial intelligence companies.

For instance, 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 when data sources connect to platforms. They should offer inputs for AI and machine learning algorithms. Availability of good quality data is critical here.

Besides data identification, businesses must ensure that data is up-to-date and complete to achieve 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

Businesses must collaborate with security professionals or threat researchers through crowd-sourced platforms. Enterprises can even create proprietary platforms to collaborate, discuss, and share latest data threats.

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

After all, improving the logic of AI algorithms is detrimental to detect threats efficiently. The best security teams 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 integrating 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.

Making 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. Cybersecurity experts believe 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 cybersecurity through key performance indicators.

The Bottom Line

Cybersecurity 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.

Learn about our cloud transformation services and related content.

2. Reinventing Cybersecurity with Artificial Intelligence, Capgemini Research Institute

POSTED ON: February 5, 2020 TAG: Digital Engineering

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