Is a ZeroTime/Zero-Trust Approach the Perfect Cybersecurity Model?

Many people are familiar with zero-trust network security, which is the assumption of a default state suspicion until authenticated access is granted to network resources. Much less know about zero-time security.

Zero-time is an approach to threat prevention facilitated by deep learning. This article explores how the progress made in the integration of deep learning in cybersecurity and how a zero-time/zero-trust approach helps organizations to achieve higher-level security.

Modern-day cyber attackers now exploit AI, ML, and automation to scale malware infection and evade detection. These threats have more far-reaching outcomes for victims. For organizations to protect themselves from these advanced threats, they have to use superior tools and technologies. That's where deep learning comes in. Deep learning helps organizations to detect threats in 'zero-time', with far greater accuracy than previous approaches.

Deep learning is a way to renew focus on 'prevention first' in a cyber world that focuses much on threat detection and incident response. More so is when it is combined with the Zero-Trust model, whereby the default state for every user is suspicion unless authenticated access is granted.

To fight zero-day attacks, it is becoming apparent that we need a combination of technologies focused on stopping external attacks in zero-time and preventing internal threats using the zero-trust idea.

Cybersecurity is a very complex space. Cybercrime becomes more sophisticated by the day and most times crime fighters have to play catch-up. This is largely due to the dynamism of malware. With new varieties emerging every day, the classification of malware (a necessary step to threat detection) becomes increasingly difficult. More so, through automation cybercriminals can (and have) replicate, modify, and scale existing malware to gargantuan proportions. 

Hence, the need for a model that can identify new variations of malware without human assistance. And that is deep learning. One of the limitations of machine learning is the high rate of false positives in threat detection.

Unnecessary alarms can make the IT team overreact while missing some of the most dangerous threats. On the other hand, deep learning is a subfield of machine learning (which is a subfield of artificial intelligence) that replicates the function of the human brain using neural networks.

Benefits of deep learning in cybersecurity include real-time detection and prevention, real-time classification, prediction of unknown (future) threats, operation on any device or operating systems, and connectionless edge deployment. Deep learning, with its capability of unsupervised learning from raw unstructured data, uses the variability of malware (which should naturally be a challenge) to improve its capacity to detect threats in zero-time.

Apparently, in the wake of advanced persistent threats (APTs), the prevention-first mindset has made a comeback in cybersecurity. The fact is that detection does not provide enough protection. Why? 

According to IBM’s 2020 Cost of a Data Breach Report, the average time to detect a data breach is 207 days while containment takes a further 73 days. If the data breach is caused by a malicious attack (as against a system glitch or human error), the average detection time increases by 23 days. Basically, an organization can remain under an attack for over 6 months and not know it, despite having detection tools. 

While (early) threat detection is important, adopting a prevention-first principle provides the best defense against attacks. Zero-Time threat prevention is unlike threat detection and incidence response technologies. Unlike the latter two that focus on eliminating the threats after it is noticed on the network, zero-time threat prevention will stop the threat before it enters the organizational network.

Cyber attackers often use evasive techniques but deep learning has the capacity to not just train itself to recognize known threats, but also unknown threats. Hence, deep learning is useful for predicting threats with precise accuracy.

Zero-time security does a good job protecting organizational networks from external cyberattacks, but it is limited in protecting endpoints. This is a gap that has been traditionally closed by legacy technologies such as firewalls and VPNs. Platforms like Express VPN, Switcherry VPN and Surfshark provide military-grade encryption.

Legacy VPN platforms like Express VPN and Surfshark have been well-rated over the years as a result of their many security features which include fast speed, strict no logs policy, DNS leak protection and so on. Switcherry VPN on the other hand, which is a fast-growing next-gen VPN platform provides the above-mentioned security solutions and is also looking to configure next-gen technologies like Software-Defined Perimeter technology (SDP) and Secure Web Gate (SWG) into their system along with inbuilt ad blockers. 

These emerging next-gen techs, coupled with the already existing features of VPNs, will help for a more solid zero-trust framework for VPNs and it will go a long way in securing endpoints against next-gen cyber threats.

Currently, the zero-trust standard for remote access authentication is the big thing in endpoint security. It is the basis of advanced technologies such as Software-Defined Parameters, Secure Web Gateways, Next-Generation Firewalls, etc.

The convergence of these two principles (zero-trust and zero-time) provides solid and comprehensive security where zero-time ensures external protection while zero-trust ensures internal protection. This is the kind of relationship that births a balanced cybersecurity approach. Even with zero-trust, an organization is still at risk of 24% of data breaches caused by external threats (malware/ransomware). Hence, both principles of zero-time protection and zero-trust security must complement each other in the war against cyber attacks.

The main edge that deep learning has over machine learning and AI is that it does not rely on human skills and is not limited by a person’s expertise. Deep learning does not eliminate every cybersecurity problem. But it does a good job of eliminating the current limitations of traditional AI and machine learning cybersecurity techniques.

Deep learning in cybersecurity is still at the earliest stages of its development. Deep Instinct claims to be the first company to apply deep learning to cybersecurity. But more are springing up (such as Sophos, Buguroo, etc.) In the coming years, one can expect the use of deep learning in cybersecurity to become mainstream. And of course, the technology will improve.