Cybersecurity is a primary concern today and is evolving in the face of digital contactless payment systems, accelerated business transformation, new multi-channel experiences, and people working from home as a result of the COVID-19 pandemic. New security threats are emerging and organizations are looking to technologies like quantum computing and machine learning (ML) to keep ahead of the new threat landscape.

Digital businesses are using cybersecurity as an important enabler of customer experiences, risk management, supply chain orchestration, and product value proposition. For example, cybersecurity can be designed into seamless, secure customer interaction processes, IoT products, and end-to-end digital value chains. 

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Quantum compute and ML present the possibility of groundbreaking progress in the cybersecurity space. They can offer more robust opportunities to protect crucial and sensitive data, and can be leveraged to improve existing cybersecurity strategies. 

Quantum Computing: Applications and Use Cases 

As the world becomes increasingly connected and automated and digital infrastructures and ecosystems advance, cybersecurity is a growing need and corresponding challenge.

ML is already reshaping cybersecurity across industries, including healthcare, supply chain, financial services, entertainment and media, manufacturing, and ecommerce. When deployed via an intelligent platform—for example—ML enables organizations to better analyze threats, respond quickly to security issues, leverage automated threat detection, identify potential data breaches, and maintain pace with constantly evolving risks. 

As a new disruptive technology, quantum compute can reduce computer processing down to mere minutes, and has the ability to solve problems that have previously been unsolvable using existing computing technologies. Also, it might accelerate ML and improve its effectiveness for cybersecurity, such as hastening the classification process for massive quantities of data.

Some emerging use cases for quantum computing and machine learning in cybersecurity include:

  • Quantum metrology: Quantum measurements involve highly accurate manipulation of particles to identify subtle changes in information. Quantum metrology could enable new types of radars, cameras, and other systems, which when applied in defense and national security use cases, might offer better ways to detect things like stealth aircrafts via quantum radar, or underground facilities via quantum gravimetry. It can also provide new types of location detection that does not depend on GPS signals—which can be easily tampered with.
  • Cryptography: An essential aspect of cryptography is random number generation. To break it down: pseudo random number generators (PRNGs) and true random number generators (TRNGs). Quantum technology number generators (QRNGs) can be regarded as a specialized instance of TRNGs, whereby the data is the product of quantum events. But QRNGs, unlike classical TRNGs, denote actual random numbers via the exploitation of the randomness in quantum physics. A random number generator offers robust security because the generated number is impossible to predict.
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  • Quantum-secure communications: Quantum principles are used in quantum communications to build new types of systems, and new means for securing critical infrastructure. A highly developed approach for this is quantum key distribution (QKD), which utilizes reduced laser pulses to share a traditional encryption key between users. QKD could be used to secure sensitive government communications, and later on may revolve around the development of quantum computer networks.
  • Quantum machine learning (QML): The expense of training deep models is increasing alongside growing data volumes and complexity. QML might drive quicker, more energy, cost, and time efficient ML algorithms that could generate highly effective algorithms for detecting and resolving novel cyberattack techniques. 

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Balancing Benefits Against Potential Risks of Quantum Computing

The other side of this coin is the possible security risk of quantum computing. It’s powerful computing capabilities could be leveraged for malicious intent to break into public encryption standards widely relied on by almost all devices, enterprises, encryption services, and the broader digital economy, which is deeply concerning for both government entities and businesses

While the majority of businesses won’t be able to procure consequential value from this technology for at least a decade, some will grasp some gains over the next five years. Further exploration into governance and policy protocols, quantum literacy, and quantum-resistant cryptography may be helpful in addressing the potential risks. But though quantum computing might render certain encryption protocols defunct, it also represents the potential to drive a markedly improved degree of privacy and security.

Analysts and researchers have been making efforts to develop quantum-safe encryption, and new techniques like post-quantum cryptography. As quantum computing provides a likely solution for encryption and cybersecurity threats, forward-thinking organizations should begin to deepen their understanding of crypto agility

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Planning for the Cybersecurity Revolution

The quantum computing and ML-propelled implications for cybersecurity are incumbent. Organizations and data science professionals must begin to engage in the short and long term roadmap of strategy development in this domain for both risks and benefits. 

Professionals who want to get started on — or stay on top of — this remarkable field and the latest technology breakthroughs should check out Simplilearn’s online bootcamps in Cyber Security, AI and Machine Learning, and Data Science & Business Analytics, and more.

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