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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.

The Importance of Security Information and Event Management in Business

We live in a digital era when modern businesses rely mostly on their IT infrastructure in order to conduct their daily activities. Of course, the reliance on IT brings a few advantages to organizations which become more streamlined and productive, but at the same time there is a persistent challenge that all businesses have to face: cybersecurity threats and incidents.

Cybersecurity incidents are not something unknown for the enterprises. Most businesses try to ensure the security if their IT infrastructure by establishing special safeguards. However, just slapping up some firewalls or subscribing to an antivirus software is not a serious approach anymore, not only because they are ineffective but also because the cybersecurity threats are continually evolving, and criminal hackers become more sophisticated. So, to resolve this problem, businesses have begun to turn to a more robust method of managing the security of their IT infrastructure: security information and event management (SIEM) software.

How does SIEM work?

Security information and event management (SIEM) software gives security professionals both insight into and a track record of the activities within their IT environment.  It is a group of complex technologies that provide a centralized view into a network’s infrastructure. SIEM provides data analysis, event correlation, aggregation and reporting, as well as log management.  While SIEM technology has been around for more than a decade, it becomes a critical component of a comprehensive security strategy in today’s threat environment.

The function of SIEM in cybersecurity is to provide a complete overview of a business’ entire IT infrastructure. Log data from applications, devices, networks, firewalls, antivirus software, wireless access points, and similar sources are collected to identify, analyse, and categorize different types of security threats the business may experience. SIEM products also provide dynamic, up-to-date information on the overall health of a business’ security system. This information can then be used to complete security compliance reports, analyse areas of weakness, and strategize solutions that may best protect the business’ entire IT systems in the future.

How Does a SIEM Help with Log Monitoring and Management

Effective log management is essential to an organization’s security. Monitoring, documenting and analyzing system events is a crucial component of IT security. Log management software or SIEM’s automate many of the processes involved. A SIEM handles the two following jobs that prior to today’s SIEM’s were handled individually:

  • SIM – Security information management provides long-term storage as well as analysis and reporting of log data. This was and is still tricky and time-consuming if you must build your own connectors to your IDS/IPS, Firewalls, DLP solutions, Application servers and so many other log generating assets in your IT environment. Most SIEM’s have some connectors out of the box today.
  • SEM – Security event manager provides real-time monitoring, correlation of events, notifications and console views. This is the key benefit of SIEM’s because a good SIEM will turn data into insights and a great SIEM, tuned correctly will turn insights into visual dashboards to assist analysts in uncovering anomalies and threats.

Effective SIEM solutions rely on logs from all critical components of a company’s business and network. These should include all firewall logs, logs from intrusion detection systems and antivirus system logs. As well, logs from primary servers should be included, particularly key application and database server logs along with the active directory server logs and web server logs.It is also important to protect your sources of log information, particularly when attempting to prove any legal culpability from computer misuse. This is because cyber attackers can try to delete or falsify log entries to cover their activity in your system.

Why SIEM is important and beneficial for the business?

To establish a capable cybersecurity team, SIEM solutions are a must-have for businesses in any industry. Today’s enterprises need a solution that can centralize, simplify, and automate security workflows to enable better analytics and incident response procedures. The key important pillars of a Modern SIEM are:

  • Incident Detection

SIEM enables the detection of incidents that otherwise would go unnoticed. Not only can this technology log security events, they have the ability to analyze the log entries to identify signs of malicious activity. And by gathering events from all of the sources across the network, a SIEM can reconstruct the series of events to determine what the nature of the attack was and whether or not it succeeded.

  • Efficient Incident Management

An SIEM solution can significantly increase the efficiency of incident handling, saving your security professionals time and resources. More efficient incident handling ultimately speeds incident containment, therefore reducing the extent of damage that many incidents cause. A SIEM improves efficiency by enabling rapid identification of all sources that were affected by a particular attack and by providing automated mechanisms to attempt to stop attacks that are still in progress.

  • AI Cybersecurity

In recent years, advanced technologies like machine learning have made SIEM platforms more robust. It gives the companies the power to defend their businesses with complex threats before they become irreparable. It accurately analyzes event correlations for unique patterns that may lead to the detection of complex concerns over information and system security.

  • Better Security Analysis

With SIEM solution, organizations get to integrate risk assessment services. SIEM tools make it possible for you to analyze network behavior in different circumstances and factors based on security sources for that particular condition.

  • Proper Categorization

Businesses can categorize and standardize network logs for effective monitoring and achieve a responsive workflow with in-depth visibility of your backups and security. It provides your IT team with access to additional features like quick data encryption, system access management, SSO integration, and other quality management services.

Businesses now have multiple services available in the market that can accommodate any SIEM requirements. Some of the most powerful software are IBM QRadar and Splunk Enterprise Security. Based on your system requirements, you can decide what SIEM features you want from your SIEM solution. Moreover, considering elements like budgeting, storage array, customization preferences, and training needs is also essential. And finally – businesses must determine their current resource capabilities before integrating any SIEM tool into their systems.

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.

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.