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The Growing Importance of Machine Learning in Cybersecurity

The need for increased data security was recently put as a top priority on the global cybersecurity agenda by the EU General Data Protection Regulation (GDPR). This regulation imposes all companies having access to the personal data of the EU citizens to adopt more secure approaches to managing customer data, protecting against its accidental loss or illegal destruction, theft, and unauthorized disclosure. According to a number of cyber security reports, more than 50  percent of enterprises across the world have at least one incident of a major data breach or network attack annually. As more innovations in digital technologies end up in wrong hands, hacker attacks become more and more sophisticated and disastrous. That is why more companies rely on the AI/ML cybersecurity innovation. However, how can Machine Learning actually be leveraged to improve cybersecurity and data security, in particular? This article will explain the answer in details.

What Is Machine Learning and why it is so important?

Our partner IBM defines Machine Learning as a branch of Artificial Intelligence (AI) and computer science which focuses on the use of data and algorithms to imitate the way that humans learn, gradually improving its accuracy. The machine learning process begins with observations or data, such as examples, direct experience or instruction. It looks for patterns in data so it can later make inferences based on the examples provided.

ML has proven valuable because it can solve problems at a speed and scale that cannot be duplicated by the human mind alone. With massive amounts of computational ability behind a single task or multiple specific tasks, machines can be trained to identify patterns in and relationships between input data and automate routine processes. Machine learning models are able to identify data security vulnerabilities before they can turn into breaches. By looking at past experiences, machine learning models can predict future high-risk activities so risk can be proactively mitigated.

Use of Machine Learning in Cyber Security

Cybersecurity is a set of technologies and processes designed to protect computers, networks, programs and data from attack, damage, or unauthorized access. In recent days, cybersecurity is undergoing massive shifts in technology and its operations in the context of computing, and data science is driving the change, where machine learning (ML), a core part of Artificial Intelligence can play a vital role to discover the insights from data.

Machine learning helps automate the process of finding, contextualizing, and triaging relevant data at any stage in the threat intelligence lifecycle. This could mean anything from finding dark web forum posts indicating a data breach, to detecting suspicious network activity in real time. To better understand previous cyber-attacks, and develop respective defence responses, ML can be leveraged in various domains within Cyber Security to enhance security processes, and make it easier for security analysts to quickly identify, prioritise, deal with and remediate new attacks.

The following points are just a few examples how Machine Learning can be used to aid security:

  • Automating Tasks

A great benefit of ML in cyber security is its capacity to automate repetitive and time-consuming tasks, such as triaging intelligence, malware analysis, network log analysis and vulnerability assessments. By incorporating ML into the security workflow, organisations can accomplish tasks faster, and act on and remediate threats at a rate that would not be possible with manual human capability alone. Automating repetitive processes means that clients can up or down scale easily, without changing the manpower needed, thus reducing costs in the process.

  • Threat Detection

Machine learning algorithms are used in applications to detect and respond to attacks. This can be achieved by analysing big data sets of security events and identifying patterns of malicious activities. ML works so that when similar events are detected, they are automatically dealt with by the trained ML model. In the case of security, by analysing millions of events, ML technologies learn to identify deviations from established norms. Instead of countering the latest threats after they have been identified like traditional systems do, ML can identify anomalies as they emerge. Against the background of quickly evolving threats, it’s not hard to see how valuable this is for protecting against data breaches and maintaining uptime and business continuity.

  • Increasing the speed of detection and response

AI and machine learning can easily analyze massive amounts of data in seconds, making it far faster than manually detecting threats. What’s more, they can implement patches and remediate threats in near real-time, dramatically improving response times. With the ability of today’s cyberattacks to quickly penetrate an organization’s infrastructure, razor fast detection and response is key to success.

  • Fraud Identification

Within the banking and finance industries, AI and ML models are being used as effective tools in identifying and preventing advanced attempts at fraud. Through predictive forecasting, models can build threat profiles to prevent fraud before it happens.

  • Provide endpoint malware protection

Algorithms can detect never-before-seen malware that is trying to run on endpoints.  It identifies new malicious files and activity based on the attributes and behaviors of known malware.

  • Protect data in the cloud

Machine learning can protect productivity by analyzing suspicious cloud app login activity, detecting location-based anomalies, and conducting IP reputation analysis to identify threats and risks in cloud apps and platforms.

  • Improving your overall security posture

With AI and machine learning, cybersecurity gets stronger over time as more data is analyzed and these technologies learn from past patterns to become more proficient at identifying suspicious activity. They also protect an organization’s infrastructure at both the macro and micro levels, creating more effective barriers than can be achieved using manual methods.

Cybersecurity is an essential consideration for any organization – especially as the world progresses digitally so fast. Cyber attacks are getting more sophisticated, requiring companies to up their game and respond in the same way. Whether preventing a future attack or analyzing why and how one happened in the past, using AI and ML models creates a faster, more comprehensive cybersecurity response.

How Does Artificial Intelligence Help in Identity Verification?

Nowadays Identity theft is regarded as a growing problem. With the increase in online shopping, the number of online identity theft increased rapidly. According to Internet security report from 2019, cybercriminals diversify their targets and use smart methods to commit identity theft and fraud.  Unfortunately, the number of fraudulent transactions and massive data breaches increases as the fraudsters and cybercriminals become more sophisticated. For most businesses, it is essential to identify and verify the identity of their client in order to decrease the potential risks. To deal with that challenge, various ID scanning and security solutions have been implemented using Artificial Intelligence (AI).

Automated Identity Verification

Thanks to the modern AI technology, that process can now be automated. A stable AI system can solve this time consuming task in a matter of a few minutes. So, now let’s see how an AI based identification and verification solution can help solving this problem.

From one side, Artificial intelligence enables computers to make human-like decisions and automating a particular task. It empowers everyday technologies like search engines, self-driving cars, and facial recognition apps. AI cannot only deter online frauds and scams, but IT plays a pivotal role in making payment frauds a thing of the past if used with appropriate intelligence. From the other site, machine learning and deep learning make it possible to authenticate, verify and accurately process the identities of the users at scale. Here are some ways AI and machine learning are used to scale identity verification.

KYC (Know Your Customer) checks

First of all, KYC (know your customer) checks is a common process used in most businesses. The aim behind these checks is to ensure that they know what their customers are, what type of activity is expected from a certain customer and also the type of risk they could bring to the business. Such checks are important to ensure the sustainability of the business. This, however, is a long and a tedious process. With the added advantage of machine learning algorithms, an AI-powered system can detect any attempt of document fake information on an identity document at much more quicker pace and with much efficiency as compared to a non-AI system or a manual review process. The biometric features captured during the facial verification process can be cross-matched with the face image present on an identity document. This establishes the ultimate verdict either against or in the favor of the identity of the incoming user.

AI and Biometric Authentication

Biometric authentication is used to fulfil KYC and KYB (Know Your Business) compliances. It uses fingerprints, eyeball scanning or face scanning to verify a person’s identity. It can also be used to authenticate the employees at the workplace. The old method of authentication like passwords or PIN code has long been finished due to biometric authentication. With the help of AI, biometric can create data-driven safety protocols and the verification solution cannot be manipulated by fraudsters. Here we will list some of the ways AI can work with Biometric verification:

Facial Recognition

Facial recognition processes can be tricked easily with a picture or a video of the owner. There are many cases where the system has been fooled were due to 2D facial recognition. This is where AI plays an important role by using 3D biometric facial recognition technology. It detects the face of the person and learns from many different pictures. AI can also detect if a person is using a face mask, using a picture of a picture, a picture of the screen, or using a tampered document with a fake picture. That’s why AI matched with biometric makes a perfect solution for bio authentication.

Voice Recognition

AI can be used to recognize voices in these biometric systems. Different voice patterns like speed, tone, accent, etc. can be analyzed as well. AI can evaluate a person’s voice for biometric verifications.

Keystroke dynamics

Just like writing, the typing pattern of the person differs as well. AI can recognize a person from their typing pattern and verify their identity. It uses dwell time, speed, and fight time. Dwell time is how much time the user puts in pressing a key, and fight time is a time in releasing a key and pressing another key. This system can also identify a person with their frequently used keys.

  • For more information about Identity verification, check out here.

Is Artificial Intelligence a Factor for Improving Identity Management and Security?

In today’s global and highly interconnected business environment people and companies collaborate constantly together. From one side, the business becomes more productive and efficient, but from the other side grows probability for the company to become a victim of a data breach or another cyber threat. Determining who should have access to what information is a hard task for many businesses and leaving that problem aside could make their systems vulnerable. That is why the importance of a smart and mature Identity & Access Management (IAM) strategy shouldn’t be underestimated. Researches from analyst companies report that more than 70% of organizations do not have a serious approach to IAM. That means that the risk for these organizations to get suffered from a data breach is twice as high compared to organizations that have their IAM strategy applied. Research reports also show that the smarter an IAM approach is, the smaller is the security risk.

IAM against data breaches

As mentioned above, for many organisations, IAM is a critical weapon in their cyber security arsenal. It is a great solution to mitigate against data breaches as well as manage the additional risks coming with remote working and Bringing Your Own Device (BYOD). Identity and Access Management (IAM) involves tracking the behaviour and actions of each individual and asset in the IT environment, specifically your system administrators and mission-critical assets. IAM enables individuals to access the correct resources at the right times for the proper reasons, which requires significant systems integration so that all platforms have the situational awareness necessary to properly enforce policy. If properly implemented, IAM can drastically increase visibility and security.

As we look ahead to the rest of 2021, securing identity access will once again be everywhere, but we are predicting that with the help of artificial intelligence and machine learning (AIML), there will be a more positive narrative to creating and managing an immutable digital identity. New AIML authentication technologies that continuously protect pre-, during and post-authorization, while leveraging individual behaviours in a secure and private manner will become mainstream, leaving cybercriminals in the dust.

How can AI improve Identity Management and Security

AI and machine learning (ML) technologies can be a major help for effective IAM and can help to avoid a lot of problematic situations. These technologies can assist enterprises to grow from an overly technical approach of access management into a form of access management that is understandable on all levels within a business.

  • Advanced analytics

Analytics in a combination with artificial intelligence can provide more focus and contextual insights so that both technical and non-technical employees can work more time efficient. Modern technologies provide ways to learn new insights and automate processes, which are able to drastically speed up the existing IAM compliance controls. They can detect anomalies and potential threats, without the need of security experts. This gives employees the needed information to make correct decisions. Such progress is crucial, especially in the area of fraud detection and in the area of combating insider threats. In this way the enterprises are continuously in control, continuously secure and compliant.

  • More precise access control

Moving on from biometric passwords, it is not hard to conceive that AI could identify a user with extra security by using sight and sound. Rather than checking against pre-defined credentials, a machine would be able to understand and confirm whether a person was who they claimed to be, by using visual and aural clues. It could also learn when to grant access, and act accordingly. Permitting access on the basis machine learning is the logical next step on from biometric ID.

Working within a user’s access permissions, AI systems could also monitor in a real-time any unusual or irrational behaviour. They could detect whether a user is trying to access a part of the system they wouldn’t normally or downloading more documents than they usually would. The rhythm of a user’s keyboard and mouse movements could be observed to identify irregular or uncommon patterns. These security policies allow the companies to safely conduct their business and to rely on a better breach detection and prevention.

  • Automation and Flexibility

 AI has the capability to monitor subtle details of users’ actions, so it’s possible to automate authentication for low-risk access situations and in this way it offloads some of the burden of IAM administration from the IT department. Considering these details before granting network access makes IAM contextual and granular and can control potential problems caused by improper provisioning or deprovisioning. AI-powered systems are able to apply appropriate IAM policies to any access request based on needs and circumstances, so that the IT department doesn’t have to waste time figuring out the basics of “least privilege” for every use case or resolving problems with privilege creep.

  • Going Beyond Compliance

Many enterprises make the mistake when thinking that complying with security and privacy regulations is sufficient to keep hackers away. Actually these laws are not enough to meet the security needs of every organization. The basics of compliance refers to ensuring information is only accessed by those who need it and ignoring everyone else. The flexible and adaptable nature of AI-powered IAM is very helpful in these situations. Due to the fact that AI and ML constantly monitor traffic, learn behaviors and apply granular access controls, enterprises face less of a challenge when enforcing security protocols, and it becomes difficult for hackers to get any use out of stolen credentials.

AI is no longer some special idea that nobody can realistically implement. It becomes a trend in the cyber security environment. The high degree of interconnectivity, the increasing number of human and device identities and the common practice toward global access will force the enterprises to incorporate smarter technologies into security protocols. And to implement a risk-based approach to Identity and Access Management (IAM), the enterprises will need advanced identity analytics powered by Machine Learning (ML). Best practices across the industry have proven that ML based identity analytics delivers significant improvements to IAM architecture and program management.

The Interaction Between Artificial Intelligence and Identity & Access Management

For many organisations, Identity & Access Management is a critical weapon in their cyber security battle. It is a great and robust solution to mitigate against data breaches, as well as manage the risks that come with remote working and Bringing Your Own Device – BYOD. IAM is constantly evolving across critical functions including data security, authentication, synchronizing internal data, enabling consumer contact preference management and meeting privacy compliance requirements.

The importance of a clever and mature IAM strategy shouldn’t be underestimated. Deciding who should have access to what information is difficult for many businesses and this challenge leaves their systems vulnerable. According to a Forrester research report, 83% of organizations do not have a mature approach to IAM. The risk that these organizations meet problems with a data breach is twice as high compared to organizations that have their IAM strategy. The report also presents a direct correlation between smarter IAM approaches and reduced security risk, improved productivity, increased privileged activity management and greatly reduced financial loss.

What is the Approach to Artificial Intelligence in IAM?


In the past several years, Machine Learning successfully develops due to its unique features like adaptability, scalability and ability to handle unknown challenges and to reduce human effort and intervention. AI and Machine Learning technologies could be a significant support for effective IAM. These trendy technologies could facilitate enterprises mature from excessively technical access management to access management that’s understandable on all levels.

Modern technologies provide ways to learn new insights and automate processes, which significantly speeds up the existing IAM compliance controls. They can detect anomalies and potential threats, without the need for a large team of security experts. This gives employees (technical and non-technical) the information needed to make correct decisions. Such progress is crucial, especially in the area of anti-money laundering and fraud detection, but also in the area of combating insider threats. That’s why it could be said that AI can serve as a lever to improve the enterprises’ IAM workflow and that ability makes it increasingly important in cybersecurity and Identity and Access Management.

  • AI monitoring and increased visibility

As business systems become more interconnected the need for seamless, continuous, and accurate access to information will become increasingly important. For that reason, AI advanced authentication systems will play a huge role, especially when collecting and analysing the information much faster than humans. Working within a user’s access permissions, AI systems could constantly monitor users as they move around the network, but they could also monitor any unusual, irrational or variable behaviour. They could detect whether users are trying to access a part of the system they wouldn’t normally or downloading more documents than they generally would.

  • Automation and Flexibility

Because AI monitors the details of users’ actions, it’s possible to automate authentication for low-risk access situations. In this way it can offload some of the burden of IAM administration and can prevent the “security fatigue” among users. AI is capable of looking at the total set of circumstances surrounding access requests including time, device type, location and resources being requested.

Considering these details, before granting network access, it makes IAM contextual and granular and can control potential problems caused by improper provisioning or deprovisioning. AI-powered systems are able to apply appropriate IAM policies to any access request based on needs and circumstances, so that the IT department doesn’t have to waste time figuring out the basics of “least privilege” for every use case.

  • More effective regulatory compliance

Enterprise software applications which integrate AI can increase the efficiency and effectiveness of regulatory compliance programs across a variety of industries. Many enterprises believe that complying with security and privacy regulations is sufficient to keep hackers away, but that’s not enough to meet the security needs. The basics of compliance means ensuring information is only accessed by those who need it and rejecting everyone else.

Implementing compliance rules for new security laws can be a burden, and noncompliance is a common practice. The flexible, adaptable nature of AI-powered IAM is useful in these situations. AI and ML constantly monitor traffic, learn behaviours and apply granular access controls, so enterprises face less of a challenge when enforcing security protocols, and it becomes difficult for hackers to get any use out of stolen credentials.

Nowadays hackers are getting better and braver in infiltrating networks. Detecting unauthorized access attempts requires detailed scrutiny which could not be performed precisely by human monitoring. This is the reason why companies rely on artificial intelligence technologies to implement better IAM practices for improving access security and maintaining the integrity of user identities.When AI and ML are performed with the appropriate monitoring and reporting tools, it becomes possible to visualize network access and reduce overall breach risk using intelligent and adaptable IAM policies.In the highly competitive world of global finance and regulated industries, investing in AI and ML can increase the accuracy and efficiency of compliance systems, as well.

How Artificial Intelligence and Machine Learning Ensure Successful Business Transformation?

As the digital world continues to conquer the physical world, the scope of digital transformation within businesses is constantly increasing. We live in an era of new business models, streamlining operations, and rapid development of new services and products. This kind of transformation is more and more determined by the use of Information and Artificial intelligence, and Machine learning. They not only improve the customer experience, but also change the way companies operate.

Artificial Intelligence (AI) and Machine Learning (ML) are important pillars of many organisations’ digital transformations.  For example, we can now use AI for account management and customer service systems across industries. As a result you get better productivity and scalability.By definition, Artificial Intelligence is “a branch of computer science dealing with the simulation of intelligent behaviour in computers; the capability of a machine to imitate intelligent human behavior.”

Basically, AI capabilities allow a computer to analyse huge amounts of information and data to arrive at a “reasoned” conclusion about the subject at hand, simulating the human decision process, often with better decisions being made. On the basis of analysing user behaviour, streaming services can more accurately tailor recommendations and push targeted content with greater opportunity for engagement.

With the advancements in machine learning, Artificial Intelligence is considered as a key driver in digital transformation across a wide range of sectors – banking, insurance, manufacturing, retail, automotive, healthcare and government.

The influence and benefits of AI and Machine Learning 

Several industries already use AI for various applications, getting better and smarter day by day. AI and machine learning benefit organizations by establishing predictive analytics and machine learning capabilities across the entire customer life cycle, by improving data completeness and consistency across back-end systems and by ensuring data quality and regulatory compliance through data governance. All these advantage features lead to reduced costs and boosted efficiency.

Advantages of AI in Banking Sector

According to Forrester report, the customer loyalty is crucial for financial services companies to sustain business growth. Sixty percent of leaders in financial services industry expect to use big data-driven AI to maximize customer value, so that they can improve the potential to upsell and cross-sell to their existing customers. On the other hand, machine learning helps to detect credit card fraud in real time by identifying unusual patterns in individuals’ transactions.

For the last few years, the banking sector has become one of the main adopters of Artificial Intelligence. Most banks and financial institutions implement AI to add more efficiency to their back-office and mitigate security risks. Thanks to AI the banks can offer better customer support by adding virtual assistants to their instant website chatbots, voice response systems, and mobile applications.

Comprehensive digital support is of a great importance for the banks to meet customers’ expectations, so with Artificial Intelligence, they achieve greater precision and accuracy. AI enhances the satisfaction level of the customers by providing abilities for bills payment, cards management, and other support. AI can also detect fraud and improves financial security with advanced fraud prevention tactics. Artificial Intelligence works as a real-time scam solution which subsequently builds more secure environment for the business and the customers.

One of the main advantages of AI is its ability to complete tasks through automation, resulting in better productivity. Based on a machine learning algorithm, AI can quickly consume and process a massive amount of data which brings efficiency to financial services and provides scope for personalized offerings to consumers.

Taking into consideration the above mentioned AI capabilities, all kinds of enterprises should consider incorporating AI into their business models. It’s not necessary to be a large company to take advantage of AI in order to provide better service to your customers – AI can help small and mid-sized businesses, as well. All of them should be involved in the digital transformation and to establish new structures, new technologies and most of all – a new mindset in relation to the customer.

If you are interested in reading why APIs are so valuable in the Digital Transformation, check out PATECCO‘s previous article here.