AI Cybersecurity

AI and Cybersecurity


Introduction: AI and Cybersecurity.

AI and cybersecurity go hand in hand in todays world. AI-supported systems can analyze thousands of data points online to identify potential cyber security threats and help with the prevention of cyberattacks. Over the past years, cyberattacks have grown in scale and complexity. As a fact, midsize enterprises get alerts for over 100,000 potential cyber threats every day.

Because of this incredible volume, cyber security experts in an organization may not be able address all the threats manually. Therefore, some cybersecurity threats can go unnoticed which may leave the organization vulnerable to cyberattacks.

If organizations want to succeed in today’s shifting cybersecurity landscape, they need to adopt Artificial Intelligence (AI) as a critical tool in their armory to prevent any cyber attacks.

AI based cybersecurity systems learn more over time and can rely on both past and present data to detect new mutations of threats—as a result, improving enterprise security every day.

Almost every business or organization is digital now and deals with critical data and infrastructure for their operations. Cyber threats not only put a serious dent financially, and time wise to their operations, but also, in terms of customer acquisition and trust.

Also Read: Artificial Intelligence + Automation — future of cybersecurity.

Advantages of AI in Cybersecurity

Detecting New Threats

AI based systems can help organizations track and detect new threats. There are so many threats originating every minute and it is becoming difficult for organizations with manual systems to track these threats and protect the organization’s data. Using AI based systems will help analyze, monitor and track cyberthreats in real time. So, it makes sense for enterprises to employ AI based systems besides general cybersecurity processes to keep track of threats and stay ahead of the ever shifting cyber threats. 

Through algorithms, AI-based systems can learn to identify new threats and ransomware. Plus, they can detect changes in existing threats, thus shielding the organization from cyberattacks.

Artificial Intelligence and Automation should be used in cyber threat detection to increase security, efficiency and help organizations be pro-active, helping them see the threats in advance and keep their infrastructure and data safe.

Keep in mind, hackers and scammers never stick to one trend. They adopt new trends now and then to bypass normal cybersecurity processes. AI makes it easier for companies to stay up to date on matters of universal and industry-specific cybersecurity threats. This way, they can make well-informed decisions. Plus, design effective security systems to shield their networks from potential threats.

Also Read: Impact of AI in Smart Homes.

Battling Bots

Bots automate repetitive and pre-defined tasks. They’ve revolutionized customer services because of their ability to work much faster than humans. Though some bots are programed by hackers to steal customer credentials, send spam, or create bogus accounts.

The net traffic of these malicious bots often seems to originate from several IP addresses. This makes it hard to pinpoint and block the source of the malicious bot traffic. Keep in mind malware bots mimic good bots, which explains why they go unnoticed.

But AI based systems will examine behavioral patterns and identify anomalies. An organizations cybersecurity team can then use the specific information to create a system that helps them stay several steps ahead of bad bots. Therefore, protecting themselves and their customers from a data breach.

Breach Risk Protection

AI systems help an organization manage its IT asset inventory, which comprises the hardware, software, and applications linked to the information system. AI also helps companies understand the strength and weaknesses of their information security system.

Factoring threat protection in the equation, a system powered by AI can accurately predict the potential of a data breach. It achieves this by determining the weak points in a company’s overall security system. As a result, the security team can divert resources towards the most vulnerable areas. AI based systems can help build a vulnerability map to help identify weal points in the defensive systems of the organization.

AI-based insights allow companies to alter their controls and processes to enhance their resilience to cybersecurity threats. As organizations delve into smarter and innovative products, they are dependent on critical data which is under constant threat. A breach of critical data can put the organization and its customers at serious risk. A combination of AI and Automation can be leveraged to counter these threats and provide insight into obscure and malicious activity on systems, networks, and infrastructure.

Better Endpoint Protection

The shift to remote working has led to an increase in the number of endpoints. This creates a serious challenge because securing thousands of devices and users from cybersecurity threats is difficult.

Traditional endpoint protection only reacts once a data breach has occurred. These systems depend on signatures, which are from the systems used by any organization. This means if the signature definitions don’t get updated, they won’t offer so much protection.

However, endpoint protection with AI is proactive and able to analyze a vast amount of network traffic, application installation, and logging info for suspicious activities. Security systems based on AI and machine language can anticipate malware and ransomware attacks and counter them in real-time. Cybersecurity attacks often occur in seconds, so many organizations can benefit from the fast-acting ability of AI-based security technology. 

Keep in mind, AI-driven security systems are not limited to a specific number of endpoints, only by the company’s budget to acquire the computer power and the system from a provider. The best part is, the security technology can even cover outside devices brought to the organization building and connected to the network.

What Cybersecurity Executives Think About AI

In recent years, we’ve seen a rapid proliferation of AI in the cybersecurity ecosystem. Most experts believe that AI-based technologies are the future of cybersecurity.

These systems can automate repetitive human activities within the space. But that’s not the only benefit, AI helps to lighten the load of cybersecurity professionals, who at the moment are in short supply.

According to a report by Capgemini Research Institute, modern companies need to leverage the power of AI to enhance their network security against ever-evolving cyber threats. The cybersecurity executives who took part in the survey think AI-enabled security technologies are necessary. This is true, considering hackers are already using AI-based hacking tools to breach networks.

According to the executives, AI will allow enterprises to respond to network intrusions much faster. Some experts believe that, in this technological era, AI is the key to shielding organizations from cyberattacks. Furthermore, the technology will make cyber analysts accurate and more efficient.

The growth of networks and increased complexity of data calls for AI-based solutions that can meet companies’ cybersecurity needs. Over time, humans will not be able to handle the vast amount of complex data and huge networks on their own. They will need AI-powered solutions. In the future, AI might even replace humans completely in making security decisions.

How AI Improves Cybersecurity

Threat Hunting

Every month, hackers create and release hundreds of malware, ransomware, and viruses for many reasons. Maybe they want to steal an organization’s confidential information or the employees’ and customers’ details. Either way, hackers never breach a network for a good reason.

Automated threat detection

Automation leverages automation and machine learning so that it can be rapidly updated, retained, and applied to the constantly changing threat cyberscape. Building a machine learning pipeline from the ground up allows the organizations to learn directly from sample data, integrating it with other threat prevention platforms and doing some pattern mapping analysis so they can all benefit from classification-optimized algorithms.

This approach means systems can continually and dynamically learn what’s “normal” in software structure, software behavior, and network traffic patterns, usage thus becoming very effective. With machine learning, millions of variables and data points can be analyzed at once to identify anomalies that could indicate an attack.

Read more: Automation in small steps.

Threat response automation

Once we have security orchestration, we can trigger a chain of responses that can help mitigate the risk of the cyber threat spreading throughout the system or better still, prevent it.

Quality of data intelligence is a challenge. Cyber threat intelligence is often prone to false positives due to the obscure nature of IoT (Internet of Things). Threats can change instantly from one second to the next. Artificial intelligence and Machine learning will help us identify a group of steps that need to be executed based on threat detection. We can attain greater accuracy if there is pattern mapping from a global list of cyber threats or threat repository.

It’s usually hard for cyber experts to identify the threats in time and shield their organizations from attacks. Therefore, if a threat goes unnoticed, it can harm the network adversely. But with the help of AI, cybersecurity professionals can analyze various data points online to identify potential threats. As a result, when a hacker launches an attack, the system can shield the company’s network in a pro-active manner.

Vulnerability Management

Today, nearly every enterprise depends on IT (information technology). But keeping IT safe is not easy with the growing cybersecurity vulnerabilities. Humans cannot manage all these vulnerabilities. So, it makes sense to rely on a more practical solution, AI and ML-powered systems.

These systems can scan articles, news, dark web forums, and more for information about new threats. Thus, allowing businesses to determine how vulnerable they are to attacks and which strategies they should implement to secure their organization.

Why (AI + Automation) is the solution?

In my opinion, AI + Automation is a great solution for the following reasons.

Cyber security = Security automation + AI

Security automation = Threat monitoring + detection + response

AI = Accuracy

Security Automation:

Smarter threat monitoring
Smarter threat detection
Smarter threat response

What is Security Automation?

Security automation is the automatic handling of a task in a machine-based security application that would otherwise be done manually by a cybersecurity or a webOps engineer.

What is orchestration?

Security orchestration is the integration of various security applications and processes together.

What is Security Automation and Orchestration?

Security automation and orchestration is coordination of automated security tasks across connected security applications and processes.

It is designed to reduce risks, operational errors, improve efficiency, and to address the Cyber Security threats that often come from erroneous use of data. Manual effort increases time and effort required for the analysis which affects incident response time which is critical in mitigating the threat.

When using manual security tasks, sifting through a large volume of data is error-prone and also time-consuming. Ideally, any security task should follow the formula —


ST = Security Task
Q = Quick
R = Reliable
A = Accurate

Automated security tasks can handle the quick and reliable part, but, fail with accuracy. Accuracy can be improved using AI, with smart testing data and real-time pattern mapping of cyber threats.

Security automation helps devOps / WebOps teams to be proactive rather than reactive. It also helps the team identify obscure attacks and be prepared for them.

Data Centers

With the help of data center AI and ML solutions, organizations can secure their data from disruptive cyberattacks. These solutions allow businesses to manage their constantly growing data storage needs and processing requirements. AL and ML tools will monitor all devices across the data centers and identify security issues before they disrupt the organization’s activities.

Network Security

A security network comprises two principal components; organization network topography and security policy development. However, managing these processes requires lots of human effort and time. Artificial Intelligence and Machine learning solutions can automate the processes.

The tools will analyze network traffic dynamics and create strategies to fit the enterprise situation. As such, the enterprise can save time and money.

Uses of AI By Adversaries

As we’ve mentioned above, cybercriminals can take advantage of AI and ML technology to launch hard-to-detect attacks that can seriously disrupt organizations’ processes.

Adversaries can hack into AI-based systems and alter how they learn and interpret the input. This way, the system can work to their advantage. They can also modify malware via AI technology to bypass ML-based antivirus and gain access to sensitive data. Additionally, AI can make popular cyber threats like phishing even more effective.

How do we deploy security automation

There are many ways to deploy this to one’s own organization and tailor it to the requirements.

Here are high-level steps of the security automation deployment.

Deployment automation
Infrastructure automation
Security monitoring tools automation
Automated threat detection
Threat response automation
Security workflow automation

Deployment Automation

Deployment automation allows applications to be deployed across the various environments used in the development process, as well as the final production environments. This results in a more efficient, reliable, and predictable deployments. Solutions that automate your deployment processes improve the productivity of both the Dev and Ops teams and enable them and the business to develop faster, accomplish more, and ultimately build better software that is deployed more frequently and functions more reliably for the end-user.

Infrastructure Automation

Infrastructure automation must start with strategy and a deep understanding of the process, which will inform automation choices. Configuration management tools should be used to automate infrastructure updates and scaling. Pair configuration management and infrastructure automation tools with a solid change management system, and your systems administrators might get an entire holiday off without worrying about alert calls. Yes, This can happen!

  • Repeatability
  • Fewer errors
  • Stability
  • Speed
  • Lower costs
  • Smarter approach

Security tools automation

Constant validation is an essential piece of security methodology and it takes place by way of continuous monitoring and alerting. A robust monitoring system helps us proactively detect issues and resolve them quickly.

AI as a partner:

Informed Decision Making
Faster Resolution
Consistent and Stable Root Cause Analysis
Predictive analysis + Contingency execution

Security Automation + AI / ML is very relevant, this technology can learn from gradual training and failures which can easily and immediately identify any abnormal behavior. This statistically scores the priority of each potential threat that should be investigated. This improves the flag detection in real time and triggers necessary remediation steps.

The idea for AI in cybersecurity is to constantly adapt to the expanding threats in the cyberspace. Humans connecting the dots, distributing data and applying it to systems is a slow and ineffective process. A mature AI system can run through millions of data points, study threat repositories, connect the dots to improve the response time of contingencies to milliseconds.

Downsides of AI in Cybersecurity

Despite the benefits of AI in cybersecurity, it comes with a set of downsides. First, organizations would require a large amount of money and resources to invest in AI-based systems and computing power.

Second, AI and ML solutions require a vast amount of data sets to learn. Collecting non-malicious codes and unique malware codes need a lot of time, effort, and investment, which most enterprises lack. Without a diverse and large database, the solution will deliver incorrect results, hampering the organization’s cybersecurity efforts.

Lastly, cybersecurity experts fear hackers will leverage AI capabilities to launch effective and sophisticated cyberattacks at their companies.

Conclusion: AI and Cybersecurity

AI has a huge potential for revolutionizing the cybersecurity space. Not only will AI enhance endpoint protection but also help in vulnerability management. But, at the same time, it’s the key to sophisticated attacks aimed at organizations. As the proliferation of AI increases, so does the explosion of network breaches. Ironically, AI and ML systems are our best line of defense against AI-enabled data breaches.