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Why API Security Is Critical In the Digital Business Era

Nowadays we are living in the era of digital business, and companies all across the world compete with one another to make the most of digital technology. Small companies also strive for being part of this trend, since it’s the need of the hour. In this context, every single aspect of digital security or cybersecurity is of critical importance for any business organization. In this article we will discuss one of the very relevant aspects of digital security, namely API security.

What is API?

Simply explained, API (Application Programming Interface) is connected with the development and deploying of applications. As a matter of fact, API works as an intermediary or a digital gateway that enables systems as well as applications to communicate and share data in a simple and easy manner. This is why APIs are central to the development and deployment of applications. But then, in the cyber world, everything that we use – every device, every application, every technology – would come its share of security risks. This applies to APIs, as well.

APIs are rich targets for security breach because they are not intended for direct access by users, but often granted access to all data within the application environment. Access is then controlled by granting specific permissions to the users making the initial requests that are translated into API calls, and having the API inherit only those permissions. This works fine until an attacker manages to bypass the user authentication process and access the downstream app directly via the API. Since the API has unrestricted access, the attacker gets visibility into everything. Just like a web application, APIs are subject to application vulnerability exploits to gain unauthorised access, steal sensitive data and launch damaging attacks.

Why API Security Must Be a Top Priority

APIs are critical to enterprises, empowering internal applications, integrating disparate systems, and providing data whenever needed. Without API, the digital economy would collapse. Business leaders must do more to protect API and the data communicated through them.API development has drastically increased in the past few years, fuelled by digital transformation and the central role APIs play in both mobile apps and IoT. This growth makes API security a top concern. In its How to Build an Effective API Security Strategy report, Gartner predicts that “by 2022, API abuses will be the most-frequent attack vector resulting in data breaches for enterprise web applications.” To protect yourself against API attacks, Gartner recommends adopting “a continuous approach to API security across the API development and delivery cycle, designing security into APIs.”

Given the critical role they play in digital transformation—and the access to sensitive data and systems they provide—APIs warrant a dedicated approach to security and compliance. APIs are the connective tissue linking ecosystems of technologies and organizations. They also allow businesses to monetize data, form profitable partnerships, and open pathways of innovation.  Additionally, APIs are hard to defend because they are highly exposed to the outside world. The amount of data that passes through the application layer makes it attractive to malicious actors. Furthermore, API hacking does not require advanced technical capabilities. Even relatively inexperienced attackers can use basic tools to discover and exploit API traffic to perform credential stuffing attacks, exfiltrate databases, change account values, or conduct denial of service attacks on critical applications.

Machine Learning and API Management tools

As discussed above, to tackle these intelligent cyber-attacks, there must be a comprehensive security solution which not only requires security capabilities, but also anomaly detection ability. Artificial Intelligence and Machine Learning are excellent tools for the development of such comprehensive and intelligent capabilities and can be used to manage challenging and emerging security threats. With the self-learning cognitive capabilities of AI and ML, security models can be developed for identifying and flagging anomalous behavior and malicious data trends. It will lead to a blocking of API attacks and abnormal behavioral patterns under various environments and circumstances. Thus, it adds continuous learning capability to APIs and anomalous behavior is flagged without prior knowledge of attacks and written policy.

With API management tools in-place, an API consumer’s behavior and resource utilization data are easily available. Organizations must understand real-time consumer behavior from existing information such as platform logging. There are machine learning capabilities which help us to classify positive against negative patterns. We must have proper tools and services in place to have these machine learning models. These models need to be trained on multiple APIs across different service providers.

Whether on your corporate network or in the cloud, securing your APIs is critical to your organisation’s overall security posture in a digitally transformed world. APIs are incredibly powerful tools that can help an organization advance its business goals and better integrate with customers, vendors and business partners. However, these tools also open up the organization’s technology infrastructure, requiring careful security measures to protect sensitive information and systems. Organizations using APIs should carefully assess the state of their API security controls and implement an ongoing API security program.

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How Does Machine Learning Help to Secure, Control and Manage Privileged Access?

Privileged access is recognized as one of the most significant risks that organizations are facing, driving them to think about not only about compliance-based, but to risk-aware strategies, as well. Compromised privileged access makes it possible for the cyber attackers to easily breach a system by being disguised as an authorized user. That allows them to remain undetected and freely pass through systems and networks.

We know that the driving force for PAM implementation is regulatory compliance. PAM solutions take care of the basic Privileged accounts requirement – privilege creeps, password management, accountability and such. But nowadays, with the rise of new tools and technologies, machine learning capabilities bring a new comprehension of Privileged Access Management.

The world’s leading research company, Gartner, reveals that “Identifying all systems and the corresponding privileged accounts is important, because every privileged account is a potential source of risk. However, this is a major challenge, as it is easy for privileged or default system accounts to be forgotten and left out. This is exacerbated by virtualization and hybrid environments that include cloud infrastructure. In such a dynamic environment, systems and accounts can easily fall through the cracks of privileged access management.” By this statement, Gartner means that some better methodologies must be adopted to prevent potential breaches from occurring due to improperly audited and secured privileged accounts and entitlements.

How Does Machine Learning Improve Admin User Experiences?

It is hard for most organisations to manually maintain and to audit privileged account entitlements. In this case the solution for the enterprises is to rely on machine learning intelligence. The best approach is applying identity analytics and machine learning to discover privileged access that poses a security risk to the organization. In this way the undocumented and unnecessary permissions can be eliminated or identified for monitoring with behaviour analytics.

1. Analysis of users-behaviour

One of the strongest features of machine learning is that it is able to constantly analyse and looks for anomalies in users’ behaviour across every threat surface, device, and login attempt. When any users’ behaviour appears to be outside the threshold of restrictions defined for threat analytics, an additional authentication is requested. Meanwhile access to requested resources is denied until the identity is verified. So, what machine learning does, is to make adaptive preventative controls possible by tracking and analysing every potential security threat and attempted breach in a real time.

2. Prevent and contain privilege attacks at endpoints

According to the Technical Manager from Happiest Minds – Susmita Shankaran – the area that is most targeted by attackers today, is an enterprise’s endpoint. “While choosing your PAM solution, you should look for solution capabilities to prevent and contain privilege attacks at endpoints. That is why companies are in a great need of PAM solution with file-based machine learning algorithms that detect and control automatic installation of the malicious application. In addition to least privilege access management, RBAC and application control, these solutions should seamlessly integrate with powerful privilege analytics engine. A mature privilege analytics engine helps to identify and secure accounts that are potentially exposed to credential theft, automatically invalidating suspected stolen credentials and immediately quarantine or terminate high-risk privileged sessions by leveraging statistical modelling, machine learning, UEBA and deterministic algorithms to detect malicious activity.”

3. Delivering excellent admin user experiences.

Machine learning also provides adaptive, personalized login experiences at scale using risk-scoring of every access attempt in real-time. That capability contributes for improved user experiences.Machine learning enables the business to implement security strategies that adapt to risk contexts in a real-time. Another good feature is assessment of every access attempt across every threat surface, and generating a risk score in milliseconds, which is a prerequisite for delivering excellent admin user experiences. That is a great security approach for many enterprises from different industries, including financial services and insurance companies, which protect their resources and networks from privileged access abuse. 

Having in mind the above mentioned information, it is obvious that enterprises today will step by step turn to ML and AI based technologies. Organizations should remember that cyberattack makes them vulnerable to financial losses and corporate reputation risk. That is why it is necessary to undertake investigating, assessing, and rebuilding the compromised systems. By applying a machine learning-based approach for proper validation of every access request, the risk of privileged credential misuse could be significantly reduced.