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The calendar just turned 2022, and we are already following new developments in cybersecurity that will significantly impact enterprises, tech companies, telcos, governments, and individuals in this year and the rest of the decade.

Given the speed with which the actual world and the world of cybersecurity change, there are already new pillars that are already considered on the radar. Today’s organizations definitely need a new security model that can effectively adapt to the complexity of the modern environment, embrace the shift to a hybrid workplace, and protect data, devices, and applications, regardless of location.

Operating in a complex cybersecurity landscape at present, our ability to detect and respond to attacks have matured compared to years back. Despite that, penetrators are also becoming smarter, more sophisticated, and more coordinated. The traditional security paradigm of building a wall around resources and data is simply not viable as entry points for attacks such as identities, devices, and networks now live outside traditional perimeters.

With a more distributed, diverse, and complex digital landscape, it’s time to look inward and outward for a win-win cybersecurity solution. Highlighting the concept of zero trust and the potential of artificial intelligence (AI) in boosting security and protection against malicious actors, we must understand the fundamentals as well as how to handle technology with caution to maximize its usability.

Never trust, always verify

Zero trust is a cybersecurity paradigm focused on the premise that trust is never achieved completely. Instead, it must be evaluated on a regular basis. It is a proactive, integrated approach to security across all digital layers that continuously verifies every transaction, asserts the least privilege, and relies on intelligence, advanced detection, and real-time response to threats.

Among its top benefits are increased data access visibility and reduced attack surface. With zero trust principles, it is a must to always authenticate and authorize based on all available surfaces such as identities, endpoints, networks, architectures, and applications. Security is intensified by limiting user access with just-in-time and just-enough-access (JIT/JEA) and implementing risk-based adaptive policies.

Moreover, by verifying end-to-end encryption and using analytics to get visibility, drive threat detection, and improve defenses, the zero-trust approach can reduce the attack surface of system breaches.

Zero trust is a mentality and a strategic initiative that makes organizations more resilient, consistent, and responsive to cyber-attacks. It is designed to protect modern digital environments by leveraging network segmentation, providing Layer 7 (application layer) threat prevention, and simplifying user-access control. Having the internal and external approaches, not only is preventing bad actors from gaining surface access part of the zero-trust equation. Most importantly, being able to detect a suspicious actor inside the environment is key to minimizing the impact of any breach.

Rooted in the principle of “never trust, always verify,” the initial step for zero trust deployment is recognizing the protect surface that is made up of the network’s most critical and valuable data, assets, applications, and services (DAAS). From here, identifying how traffic moves and understanding who the users are, which applications they are using, and how they are connecting are needed to determine and enforce a policy that ensures secure data access.

To gain visibility and context for all traffic in a zero trust model, a next-generation firewall will be present. With decryption capabilities, this security system enables micro-segmentation of perimeters and acts as internal border control. By adding two-factor authentication (2FA) and other verification methods will also increase the ability to verify users correctly.

As dynamic as the current digital transformation journey upon us, zero trust is not dependent on a location as users, devices, and application workloads are now everywhere. Hence, it requires consistent visibility, enforcement, and control that can be delivered directly on the device or through the cloud. Running on a software-defined perimeter, secure user access and data loss prevention are executed, regardless of where the users, devices, and data hosting are.

The good and the bad

The cyberattack surface in modern enterprise environments has progressed rapidly. This means that analyzing and improving an organization’s cybersecurity posture cannot be handled by manual labor anymore. With this in mind, AI and machine learning are now becoming essential to information security, as these technologies are capable of swiftly analyzing millions of data sets and tracking down a wide variety of cyber threats.

These technologies continually learn and improve, drawing data from past and present experiences to pinpoint new varieties of attacks that can occur tomorrow and in the future. By using sophisticated algorithms, AI systems are being trained to detect malware, run pattern recognition, and find even the smallest behaviors of malware or ransomware attacks before it enters the system.

In addition, AI allows for superior predictive intelligence with natural language processing which curates data on its own, allowing accurate prediction on how and where you are most likely to be compromised. In parallel, prescriptive insights from AI-based analysis enable control configuration and improvement to reinforce better cyber resilience.

The patterns that artificial neural networks learn over time can also improve security as potential threats with similar traits to those recorded get blocked early enough. The fact that AI keeps learning makes it difficult for hackers to beat its intelligence, and yet this can also be taken into an advantage for malicious purposes.

One worrying scenario of this is adversarial AI, which is a technique that attempts to fool models with deceptive data. Thus, it is believed that we cannot simultaneously have both more AI and more security, not unless we adjust how we secure software and data. Threat actors can actually exploit AI to automate target selection or attack timing to avoid detection. This is why deepfakes, social engineering, and AI-powered password guessing all pose a threat and are seen as forms of misuse of AI and ML.

Furthermore, poor cybersecurity in the protection of open-source models apparent with AI/ML may lead to hacking opportunities. Addressing the lack of digital talents as well, limitations to the dissemination and the sharing of codes could enable a more complete security risk assessment.

Nonetheless, data collection and AI algorithms are becoming the cybersecurity bedrock of today. The best way to protect and reduce any data risk is to automate and regulate processes using intelligent algorithms that can identify, learn, and understand potential threats. For AI/ML, zero trust can aid in ensuring that sensitive data residing on the cloud can only be accessed by authorized data scientists.

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