WHY AI IS A GAME CHANGER FOR CYBERSECURITY

SHEIK ABIDEEN, REGIONAL SALES DIRECTOR – META AT SECURONIX, ON WHY AI IS THE FUTURE OF CYBERSECURITY.

How can AI be used to improve cybersecurity?

Artificial intelligence in the cybersecurity market is projected to reach $38.2 billion by 2026 from $8.8 billion in 2019, according to

Markets and Markets.

Due to digital transformation, there is a massive increase in the variety and the volume of data generated today.

On-prem, cloud, IoT, and social media, dark web environments have only added to the complexities for the alreadystretched security teams. Security analysts cannot analyse or investigate events at the wire speed, resulting in missed attacks and breaches.

Analysing and improving cybersecurity posture is not a human-scale problem anymore. Due to this reason, companies are investing more and more in AI and machine learning technologies. AIbased security solutions are the only way an organisation can keep up with the changes and remain effective in combating modern threats.

Capgemini Research Institute analysed the role of AI in cybersecurity. Their report titled ‘Reinventing Cybersecurity with Artificial Intelligence’ strongly suggests that strengthening cybersecurity defenses with AI is urgent for modern enterprises.

Some of the report’s key takeaways include: • Three out of four surveyed executives say that AI/ML allows their organization to respond faster to breaches. • 69% of organisations think AI/ML is necessary to respond to cyberattacks. • Three in five firms say that using AI improves the accuracy and efficiency of cyber analysts.

The integration of AI in cybersecurity is becoming indispensable for organisations. However, the main roadblocks that slow down its adoption and deployment are talent acquisition, data complexity, and employing proper AI tools.

According to IBM, one of the most critical barriers to AI deployment is the lack of talent. About 37% of organisations emphasized the difficulty of finding people with the appropriate level of AI expertise and knowledge.

Is AI driving cybersecurity automation?

The answer is a big yes. AI is accelerating the adoption of automation in the cyber security space. Areas where AI/ML is already in use include: • User and Entity Behaviour Analytics • Autonomous Threat hunting • SOAR (Security Orchestration,

Automation, and Response) • Next-Gen SIEM and Security Analytics • Network Threat detection and response automation • XDR • Breach and Attack Simulation • Identity Risk Analytics • Threat Intelligence curation

Can AI replace human security analysts?

The answer is no; however, AI will drastically change the work cyber engineers will be doing in the future. Another technology that is coming up repeatedly in today’s conversation is RPA (Robotic Process Automation). RPA and ML are two distinct but overlapping technology areas. To be very clear, RPA is a subset of AI, and it can be used to assist AI with simple tasks. With the successful integration of ML with the RPA technology, companies can replace human analysts with routine activities. For example, SOAR (Security Orchestration, Automation, and Response) is one such technology in use today. Due to this reason, several technology innovators are investing heavily in ML and RPA technologies together.

Another factor driving AI adoption is the skills gap in cybersecurity. A Cyber Security Workforce Study from the International Information System Security Certification Consortium predicts a shortfall of 1.8 million in the cyber workforce by 2022. While this can feel like impending doom and gloom for the industry, AI, or artificial intelligence, can help quell the concerns while empowering existing cyber workers. AI and the human workforce are not in conflict with one another in this field; in fact, they complement each other.

While AI may be great for processing large amounts of data or replacing autonomous manual tasks, it will never be able to replace a security analyst’s insights or understanding of the field. Nevertheless, the future is bright for AI and humans to work in tandem at the front lines of cyber defense.

A WORLD OF POSSIBILITIES

WALID GOMAA, ACTING CHIEF EXECUTIVE OFFICER AT OMNIX INTERNATIONAL, EXPLAINS WHY DIGITAL TWINS TECHNOLOGY, WHEN DONE RIGHT, HAS THE POTENTIAL TO RESHAPE BUSINESS STRATEGIES AND TRANSFORM INDUSTRIES.

What are the key trends shaping the digital twin’s market?

Digital twin by definition is a replica of a product, process or service. The concept entails mapping a physical asset onto a digital platform. Any technology that can help to capture data in real-time, consolidate & process data and then analyze data will have a great impact on the adoption of the digital twin. Technologies like IoT, 5G, 3D printers, 3D scanners, big data (data lakes), computing, AI/ML, and robotics will allow flexibly built, real-time sensed, and deeply learned digital twins deployments for a variety of purposes.

What kind of problems can be solved by digital twins?

There are many areas where digital twins can add value. Take predictive maintenance as an example, by getting real-time data feed from systems, businesses can proactively identify and predict real-time problems. This will help businesses to schedule predictive maintenance ahead of failure or solve a real-time problem. This will lead to improved efficiency and minimise downtime which will have a direct impact on the profitability of the operations.

In healthcare services, sensorgenerated data can be used to track a variety of health indicators and generate key insights which will help in enhancing citizens’ wellbeing.

In the construction business and with the advancement in 3D digital twin technology, building information modeling (BIM) and virtual design and construction (VDC) are being driven to another level. Now, a 3D digital twin is being used throughout the entire lifecycle of properties – from preconstruction all the way through to maintenance and operation. This will help to keep a construction project on time & on budget and eliminates risk & complexity. In the automotive industry, the digital twin can be used to improve vehicle performance and increase the efficiency surrounding their production.

There are many other problems that can be addressed in different areas like urban planning, aircraft production, and power-generation equipment.

What is the difference between simulations and digital twins?

Digital Twins and simulations both utilise similar digital models to replicate products and processes but have some key differences. A digital twin creates a virtual environment able to study several simulations, multiple processes, and provides real feedback with a two-way flow of information to produce predictions and simulations for realtime data gathering. Simulation typically studies one process, which makes the digital twin considerably richer for study. In summary, simulations can help in understanding what may happen in the real world, while digital twins compare and assess what may happen alongside what is happening.

What are some of the new use cases of digital twins?

Companies across sectors and application areas utilise digital twins to improve planning and decision-making, including manufacturing, supply chain, automotive, aerospace, infrastructure (smart cities), oil & gas, and healthcare.

In healthcare, creating a digital twin of a hospital, operational strategies, capacities, staffing, and care models helps healthcare providers examine the operational performance of the organisation. Also, healthcare providers and pharma companies can use digital twins to model the genome code, physiological characteristics, and lifestyle of patients so that healthcare companies can provide personalised care such as unique drugs for each patient.

In aerospace, with digital twins, engineers can use predictive analytics to foresee any future problem involving the airframes, engine, or other components to ensure the safety of the people onboard.

In supply chain, logistics companies can analyze how different packaging conditions can affect product delivery with the help of digital twins.