Adaptive Security Architecture – Staying Ahead of Emerging Cyber Security Threats

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As enterprises become increasingly reliant upon DevOps, Agility, and interconnected processes using IoT, new threats are constantly emerging within the cybersecurity space. The traditional ‘incident response’ approach to security, which focuses on the prevention and detection of issues after they have occurred is not adequate.

On the contrary, present-day businesses need to have a ‘continuous response’ to security and protection against threats and breaches. It is based on the realization that systems are never absolutely hack-proof and thus require constant monitoring and analysis for reducing risks.

This approach, commonly known as an Adaptive Security Architecture, was coined by Sun Microsystems in 2008, and Gartner popularized it in 2017 by regarding it as one of the Top 10 Strategic Technology Trends for 2017. Furthermore, recent developments in AI and Machine Learning have assisted the rise of risk-adaptive security solutions by opening unprecedented avenues within the cybersecurity industry.

In this blog, we present an overview of this pro-active method of cybersecurity by venturing into questions like what is adaptive security architecture, what it means for a business to have one, and so on.

However, it’s worthwhile to mention that the basis of this preventive idea is a biological immune system. Just like immunity enables organisms to mitigate threats by adapting to the changes in their ecosystem, the adaptive security architecture allows enterprises to curb attacks not after but before they occur. They both rely upon feedback for enhanced threat response abilities.

What is Adaptive Security Architecture?
Banking on the experience of past threats, the Adaptive Security Architecture is an approach of cybersecurity that studies the patterns and behaviors on the network, rather than focusing solely upon log files or security checkpoints.

As opposed to traditional antiviruses, Intrusion Defence Systems (IDS), Intrusion Prevention Systems (IPS), and firewalls, this approach takes into account the fact that development ecosystems are dynamic and not static.

Consequently, it implements intelligent, integrated security systems that can identify and respond to threats before their occurrence. It enables the organization to be aware of newly emerging threats and take necessary preventive measures.

Why Use Adaptive Security Architecture?
The actual outcomes of implementing a risk-adaptive security infrastructure may vary based on several factors – the size and capacity of the network, the degrees of risks involved in the organization’s functionality, and others.

However, the following are some of the general benefits of implementing an adaptive security architecture.

Dynamic security – Leveraging real-time monitoring and response abilities, AI-based security solutions enable a dynamic security infrastructure.

Threat hierarchization and filtering – Analysts working with an adaptive security management architecture can use effective analytics to rank threats based on the degree of risks involved and frequency of occurrence. Furthermore, ML algorithms allow them to flag risks, which often go undetected with traditional methods.

Reduced attack surface – In a risk-adaptive security ecosystem, the hard outlining of processes helps reduce the size of the potential attack surface. Doing so, it limits the risks involved for any given threat.

Faster response – Coupling security automation with business processes, ensures a speedier response to attacks.

The Four Pillars of Adaptive Security Architecture

Predict, Prevent, Respond, and Defect are the four pillars of risk-adaptive security infrastructure, as outlined by Garter. In other words, these may also be referred to as the four stages of adaptive security, which need to be implemented alongside robust compliance and policies.

Predict – Prediction involves the assessment of exposure to various risks and proceeds by prioritizing risks based on multiple metrics such as frequency and penetration. This allows enterprises to anticipate future threats while also laying the foundations for baseline systems and security postures.

Prevent – As a means of preventing attacks, adaptive security systems isolate processes using hard outlining, reducing both the risk of attacks and potential damage in the case of an attack.

Respond – In an adaptive security management architecture, the response to a threat or attack involves addressing the ongoing issue and implementing policy change for future security. In doing so, the organization must undergo strict introspection, analysis, and a detailed investigation of the breach.

Defect – Often functioning as a precursor to prevention, this element of adaptive security involves risk prioritization. Moreover, it consists identification of tasks or processes that are prone to attacks, as well as potential attacks to defect and contain them.

However, just like the adaptive security architecture itself, these elements are not siloed systems working in isolation from one another. Instead, they constitute an interoperable, cyclic process in which every step informs and leads to the next. For the success of this approach, it is also necessary that the enterprise adjusts its security posture as per the results of continuous assessment of the network’s users, systems, activities, and payload.

The Role of AI and Machine Learning in Adaptive Security Architecture
Adaptive Security Architecture wouldn’t have been possible without developments in AI and Machine Learning algorithms.

First, they relieve analysts from the need to manually analyze thousands of log files. Second, using ML, it is now possible for security systems to access massive databases, analyze suspicious behavioral patterns, and identify newly emerging threats.

Third, and most importantly, implementing AI and ML facilitates advanced security analytics. This allows enterprises to make informed decisions, enhancing the effectiveness of compliance and prevention policies. Adaptive systems backed by intelligent algorithms are self-updating, which means they can learn from past threats and adapt accordingly.

Presently, the User and Entity Behavior Analytics (UBEA) systems are one of the most commonly used adaptive security architecture examples. Using intelligent algorithms, they perform nuanced profiling of network members, including users, peer groups, devices, applications, and sub-networks. The acquired data is then analyzed for detecting patterns as well as actual or potential deviations.

Conclusion
As enterprises become dynamic, so do the potential risks. The siloed and post-incident approaches to traditional security do not work anymore. Implementing an adaptive security architecture, companies can now ensure continuous and real-time assessment of security metrics to be able to predict, prevent, or respond to emerging threats.

This approach to security frees up analysts by eliminating the need to manually process and monitor repetitive tasks. Instead, they can now focus on developing effective strategies based on the advanced and multi-metric security analytics that these systems enable.

Lastly, with further innovations in this space, adaptive security architectures may also benefit the cybersecurity of personal users and not just enterprises. The latter, however, is already reaping the benefits of implementing adaptive security, generating better value both for themselves and their users.

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