Using AI for Smarter Cybersecurity

In Cybersecurity, systems that can learn and adapt to evolving threats are much more competent than ” fine-tuned ” systems to identify these threats. Consequently, using artificial intelligence (AI) for cybersecurity provides a compelling use case.

Typical security systems like firewalls, antivirus or intrusion detection systems (IDS) can only detect threats they are already familiar with. This leaves enough room for zero-day attacks and other matured forms of malware. A security system that’s powered by AI can identify evolving threats and even adjusts its defences to restrict the danger until it can be effectively neutralized.

AI can automate threat detection and respond to security breaches more efficiently than traditional systems in cyber security, making it an indispensable aspect of modern security.

Cybersecurity & AI

A typical enterprise has multiple levels of protection in place. They have a perimeter, network, applications, endpoint, and data security. For example, they have firewalls and network security solutions that track and identify which network connection should be allowed and block the others.

If attackers bypass these defenses, they’ll be against anti-malware or antivirus solutions. Next in line may be Intrusion Detection Systems or Intrusion Prevention Systems. Now, what happens if cybercriminals make it through all these protections? If a company’s security is dependent on human monitoring alone, they are in trouble.

Regardless of non-working hours and holidays, your security system should be able to track, detect and respond to threats immediately, 24 hours for the entire seven days of the whole 365 days. AI solutions are designed to work around the clock because cybercrimes don’t follow schedules. AI can respond in seconds to attacks that would otherwise take hours or days for humans to identify.

How Artificial Intelligence (AI) can be used to deliver Cybersecurity

In today’s world, it’s challenging to keep your business data and network secure. Adopting AI to boost your security infrastructure can help you take a decisive step toward becoming safer.

When used correctly, Artificial Intelligence (AI) systems may be trained to create threat alerts, identify new types of malware, and protect critical data for organizations.

AI and machine learning are already demonstrating their utility in detecting zero-day malware, recognizing and prioritizing risks, and, in some situations, taking automated actions to repair security vulnerabilities at scale quickly.

There are several advantages to adopting AI for cybersecurity. Let’s take a look at some of the main reasons how AI can be used for cybersecurity.

IT Asset Inventory Management

Instead of manually filling spreadsheets, AI allows organizations to maintain a comprehensive and accurate inventory of all the users, applications and devices which have access to information systems.

All critical information about assets is automatically collected, including system updates, server warranty, hosted VM details, processors, memory, network interfaces, operating system and firmware updates, and more, all in one place.

Threat Exposure

Unknown threats cause colossal damage to the network. Worse than that is their impact before they are discovered and prevented. Every year, hackers launch new tactics.

RELATED: Threat Detection and Response: Best Practices

From malware attacks to sophisticated social engineering, it has become imperative to utilize tech-oriented solutions to tackle them. AI-based cyber security provides the latest knowledge of industry-specific threats ongoing globally. This helps set priorities based on what is most likely to be used to attack your enterprise.

Breach Risk Prediction

AI-based security systems can reveal where and how your network is most likely to be breached using data from asset inventory and threat exposure analytics.

This helps businesses allocate their tools and resources around the weaker areas. The AI-based analysis provides prescriptive insights which allow businesses to improve their processes and control to strengthen their cyber resilience.

RELATED: Sensitive Data Exposure: How is it different from a Data Breach?

Data Handling

A ton of activities take place on a company’s network. Even a medium-sized enterprise transfers a lot of data between the customer and the business daily.

This data needs protection against harmful software and even people within the organization. The company’s IT resources cannot manually check all the traffic.

RELATED: How to Protect your Business Data in 10 Simple Steps

Since it’s automated, AI can comb through massive chunks of data and traffic. This includes files shared, websites visited, emails, third-party software and patterns of hacker activities. AI makes use of intelligent security software which runs robust processes.

These processes can skim through big data rapidly and draw up anomalies and its solution within several minutes. The processes also effectively catch any malicious traffic masked as normal activity.

Automated Threat Detection

AI can detect threats before they become a considerable cost to the company. AI can be trained to identify an on-coming dedicated denial of service attack (DDoS) attack long before it becomes critical.

Endpoint Protection

With many organizations shifting to remote working, AI has a critical role in securing those endpoints.

Even though antivirus software and VPNs work great at protecting against remote malware attacks, they operate on signature-based detection. Companies need to keep up with the ever-evolving signature definitions to prevent the latest threats.

RELATED: Zero-Trust Network Access: Designing a Zero Trust Network

A company’s network security can land into a fix if virus definitions lag. This can happen due to a failure to update the antivirus or a lack of awareness. Therefore, if a new type of malware originates, signature-based detection may not be able to keep up and protect your network against it.

AI takes a different route for endpoint protection. It develops a baseline behaviour for the endpoint through a repeated training process. If something different from the baseline behaviour occurs, AI flags it immediately.

This includes substantial online purchases shipped to a different address than their own, a sudden change in typing speed or a sudden increase in document downloads from the user’s archived folder. This way, AI enables proactive protection against threats instead of relying on signature updates.

Self Learning & Evolving

AI is intelligent. Combining machine learning and deep learning, AI understands the behavior of your network. It detects pattern formation happening inside and creates a cluster out of them. Then, it goes on to detect any deviation from this normal pattern.

The patterns learned by artificial neural networks improve network security over time. Future threats with common traits with those detected get blocked in their paths quite early.

The simple fact that AI continuously learns how your network behaves and fine-tunes itself accordingly makes it extremely difficult for hackers to beat its intelligence.

Password Protection & Authentication

On the web, passwords are the only layer of security between our accounts and cybercriminals. While security inventions like biometrics are a huge step, their convenience is a challenge, and they can still be hacked.

AI is being used to make biometric authentication more accurate. For example, Apple’s Face ID feature on iPhone X is created using AI.

Phishing Detection

With 1 in 99 emails being a phishing attempt, phishing has become the most commonly used form of cyberattack. Machine and AI can be combined to track and detect more than 10,000 active phishing sources.

Despite their geographic location, AI and ML can identify all types of phishing campaigns. This makes the technology even more critical for today’s security posture.

Error-Free Security

Attack tactics change, but basic security measures remain the same. A human being handling these tasks may get bored and complacent along the way and expose your network to risks. AI doesn’t get tired or distracted while performing repetitive tasks.

Therefore, errors are considerably less compared to human efforts. AI mimics the most useful characteristics of humans while leaving out their shortcomings. Thus taking care of repetitive security practices could easily bore your security personnel.

RELATED: Using Blockchain for Cybersecurity

Avoidance of Bots

Today, a considerable chunk of network traffic is dangerous bots. From taking over accounts to stealing credentials to creating bogus accounts, bots can do a lot of data fraud. Automated threats cannot be tackled by manual responses alone.

AI combined with machine learning develops an in-depth understanding of network traffic. This data enables them to easily distinguish between humans, bad bots and safe bots, like search engine crawlers.

Businesses can understand how an average user journey looks and compares to a risky, unusual journey by analyzing behavioural patterns. This way, they can stay ahead of bad bots.

Protection Against Advanced Hacking Techniques

Hackers use complex and advanced techniques to breach network security. Obfuscation and Polymorphism are just some of the complicated examples.

These techniques are extremely hazardous and difficult to identify. Combine that with a shortage of security experts, and businesses have a ticking bomb in hand.

Hackers exploit human psychology to acquire personal information. AI counters this by using social honeypot. The honeypot system is an AI tool designed which acts as a decoy user to trap hackers. From there, it traces the techniques and origins of the hacker.

Vulnerability Management

As stated earlier, even an average-sized organization deals with several threats daily. They need to detect those threats and identify and prevent them from fully securing their network. Assessing existing security measures with the help of AI can help companies better manage their vulnerabilities.

RELATED: Vulnerability Assessments: 4 Crucial Steps for Identifying Vulnerabilities in your Business

AI assesses systems faster than security analysts, thus improving problem-solving capabilities. It takes on the task of identifying weaker areas in the system and allows businesses to focus on essential security measures. This enables them to manage vulnerabilities and also secure their networks at the same time.

Drawbacks of using AI for Cybersecurity

The number of resources and financial investments organizations needs to build and maintain an AI system is considerably high. Almost everything that AI systems entail is costly, resource-intensive, and ongoing.

Moreover, AI systems are trained using data sets. For a comprehensive strategy, organizations need several distinct sets of malware codes, anomalies and non-malicious codes.

Acquiring these data sets is costly and makes an investment that isn’t feasible for most organizations. Without vast volumes of data, AI can run into many false positives.

Summary

Analyzing a company’s security posture and improving it is no longer a human-scale issue. Humans cannot scale to the point of securing an enterprise-level attack surface.

AI provides the much-needed analysis, identifies malware attacks, prioritizes risks and improves network security. The most sobering point is that malicious attackers know the same things that you know and have the same tools.

This creates an even urgent need of folding AI into Cybersecurity. It has to be done now, and it has to be done right. Hence, AI is the way forward for organizations driving Cybersecurity to create a robust security posture despite all the potential drawbacks.