The enterprise attack surface is very large, and recurring growing and evolve rapidly. Depending on the sized your company, you will find up to several hundred billion time-varying signals that need to be analyzed to accurately calculate risk.

The effect?

Analyzing and improving cybersecurity posture isn’t a human-scale problem anymore.

As a result of this unprecedented challenge, Artificial Intelligence (AI) based tools for cybersecurity emerged to help you information security teams reduce breach risk and grow their security posture wisely.

AI and machine learning (ML) are becoming critical technologies in information security, because they can to quickly analyze an incredible number of events and identify variations of threats – from malware exploiting zero-day vulnerabilities to identifying risky behavior that might result in a phishing attack or download of malicious code. These technologies learn after a while, drawing in the past to spot new forms of attacks now. Histories of behavior build profiles on users, assets, and networks, allowing AI to identify and react to deviations from established norms.

Understanding AI Basics

AI describes technologies that will understand, learn, and act according to acquired and derived information. Today, AI works in three ways:

Assisted intelligence, widely available today, improves what people and organizations happen to be doing.

Augmented intelligence, emerging today, enables people and organizations to complete things they couldn’t otherwise do.

Autonomous intelligence, being intended for the future, features machines that act upon their unique. An example of this can be self-driving vehicles, whenever they come into widespread use.

AI can be said to obtain some amount of human intelligence: local store of domain-specific knowledge; mechanisms to obtain new knowledge; and mechanisms to set that knowledge to use. Machine learning, expert systems, neural networks, and deep learning are common examples or subsets of AI technology today.

Machine learning uses statistical techniques to give desktops the opportunity to “learn” (e.g., progressively improve performance) using data instead of being explicitly programmed. Machine learning is ideal when directed at a specific task as opposed to a wide-ranging mission.

Expert systems software program meant to solve problems within specialized domains. By mimicking the pondering human experts, they solve problems to make decisions using fuzzy rules-based reasoning through carefully curated bodies of info.

Neural networks utilize a biologically-inspired programming paradigm which helps your personal computer to learn from observational data. Within a neural network, each node assigns a to the input representing how correct or incorrect it can be in accordance with the operation being performed. A final output will then be determined by the sum such weights.

Deep learning is part of a broader group of machine learning methods according to learning data representations, as opposed to task-specific algorithms. Today, image recognition via deep learning is often a lot better than humans, having a variety of applications such as autonomous vehicles, scan analyses, and medical diagnoses.

Applying AI to cybersecurity

AI is ideally worthy of solve some of our most challenging problems, and cybersecurity certainly falls into that category. With today’s ever evolving cyber-attacks and proliferation of devices, machine learning and AI enables you to “keep on top of the bad guys,” automating threat detection and respond more proficiently than traditional software-driven approaches.

Simultaneously, cybersecurity presents some unique challenges:

A massive attack surface

10s or Hundreds of a huge number of devices per organization

A huge selection of attack vectors

Big shortfalls from the variety of skilled security professionals

Masses of data that have moved beyond a human-scale problem

A self-learning, AI-based cybersecurity posture management system should be able to solve several challenges. Technologies exist to train a self-learning system to continuously and independently gather data from across your enterprise information systems. That info is then analyzed and utilized to perform correlation of patterns across millions to immeasureable signals strongly related the enterprise attack surface.

It makes sense new degrees of intelligence feeding human teams across diverse groups of cybersecurity, including:

IT Asset Inventory – gaining a whole, accurate inventory of most devices, users, and applications with any usage of computer. Categorization and measurement of commercial criticality also play big roles in inventory.

Threat Exposure – hackers follow trends the same as all others, so what’s fashionable with hackers changes regularly. AI-based cybersecurity systems can offer up-to-date understanding of global and industry specific threats which will make critical prioritization decisions based not just on which might be used to attack your enterprise, but determined by what exactly is apt to be utilized to attack your corporation.

Controls Effectiveness – you should comprehend the impact of the numerous security tools and security processes that you’ve employed to maintain a strong security posture. AI might help understand where your infosec program has strengths, where they have gaps.

Breach Risk Prediction – Making up IT asset inventory, threat exposure, and controls effectiveness, AI-based systems can predict how and where you are most probably to get breached, to be able to plan for resource and tool allocation towards regions of weakness. Prescriptive insights produced from AI analysis will help you configure and enhance controls and processes to most effectively increase your organization’s cyber resilience.

Incident response – AI powered systems provides improved context for prioritization and response to security alerts, for fast response to incidents, also to surface root causes in order to mitigate vulnerabilities and avoid future issues.

Explainability – Critical for harnessing AI to enhance human infosec teams is explainability of recommendations and analysis. This is important in enabling buy-in from stakeholders through the organization, for comprehending the impact of varied infosec programs, as well as reporting relevant information to any or all involved stakeholders, including customers, security operations, CISO, auditors, CIO, CEO and board of directors.

Conclusion

In recent years, AI has emerged as required technology for augmenting the efforts of human information security teams. Since humans cannot scale to adequately protect the dynamic enterprise attack surface, AI provides much needed analysis and threat identification that can be put to work by cybersecurity professionals to cut back breach risk and improve security posture. In security, AI can identify and prioritize risk, instantly spot any malware with a network, guide incident response, and detect intrusions before they begin.

AI allows cybersecurity teams to create powerful human-machine partnerships that push the boundaries of our own knowledge, enrich our everyday life, and drive cybersecurity in ways that seems higher than the sum of the its parts.

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