Use of Artificial Intelligence for improving cybersecurity automation processes for tasks

The combination of computing power and the availability of vast information has enabled humans to delegate tasks that were once exclusive to them, such as making predictions in complex environments and decision-making. With artificial intelligence, we can interpret images to detect diseases, engage in conversations, and anticipate risks faster, such as non-payment or fraud risks when dealing with insurance. Artificial intelligence has a broad scope for action in cybersecurity, which focuses on safeguarding our digital assets.

Artificial intelligence, as a collection of predictive and self-learning techniques, is helping to improve cybersecurity. It achieves this by continuously calibrating algorithms as they are exposed to new information. The complexity and dispersion of systems that companies currently work with have shown traditional and manual methods of risk supervision, monitoring, and control to be insufficient. Moreover, the use of artificial intelligence by cybercriminals makes our systems even more vulnerable.

Cybersecurity Automation Processes

On average, workers spend three hours of their daily work performing repetitive tasks on the computer, often unrelated to their primary responsibilities. This can lead to human error and demotivation, which hackers may exploit to prepare cyberattack strategies, as noted in a study by OnePoll.

Throughout 2023, it is anticipated that 45% of repetitive tasks in large companies will be automated, as reported in a recent IDC Research survey from various European countries. This marks a clear shift in organizational trends, largely due to the pandemic.

Automation employs Artificial Intelligence and Machine Learning technologies to increase response speed, save time, and improve accuracy by reducing the risk of human error. The six most repetitive tasks that can be automated and protected with Artificial Intelligence include:

Back Office: Data capture, mail management, digital document cataloging, computer and software reports, or tedious invoice management are among the most disliked tasks. Extracting entities from documents or invoices is possible, reducing manual inspection time while maintaining reliability. Moreover, integrating AI into corporate ERPs minimizes errors and automates this process. AI also allows for summarizing documents and obtaining new information by comparing it with previous versions, resulting in higher profitability than manual processes.

Customer Service: AI in customer service listens and interprets messages to provide the most suitable response to customer needs. Bots initiating customer conversations are increasingly common, offering faster, more accurate responses and 24/7 availability. They detect when human intervention is required and request agent involvement, freeing agents from repetitive tasks and automating simple or common processes. AI can also automate management tasks, such as classifying incoming electronic messages and filtering spam.

Review of Damage Caused by Cyberattacks and Quality Control & Assurance: New technologies have revolutionized quality control, inspections, and anomaly detection using machine learning algorithms. They can identify damage or malfunctions in machinery or structures through image inputs, sound records, or data patterns, inspect finished products for flaws and ensure quality standards.

Image Recognition: Image recognition has become more widespread in recent years, proving useful in various industrial applications. It enables adequate supervision of work environments and security equipment, identification of risk situations, and detection of product anomalies. Image analysis saves time by automatically searching millions of records within seconds or providing real-time responses.

Transcription of Information: AI can identify specific information such as ID, dates, phone numbers, or addresses. It also facilitates sales team report generation and CRM integration. AI can transcribe entire conversations, filling gaps left by phonetic transcription and converting audio words into text.

Detection of Insights and Topics: AI can extract relevant information from opinions, reviews, and user emotions. This capability allows for identifying areas for improvement and topics of interest when generating content.

These tasks can be inundated with unstructured data, making them difficult to manage. This raw or disorganized information cannot be easily stored in predefined structures. A simple example to understand the difference is introducing data through a form on a website; the collection is done uniformly, and the data is pre-formatted. However, if it is necessary to extract data from a text document, for example, a set of personal data in the body of an email will be unstructured information that must be manually processed and structured, assigning it a structure or classification.

Current applications of Artificial Intelligence in cybersecurity in repetitive work processes

Threat hunting: Identification of threats and neutralization of cyberattacks. Traditional techniques that rely on identity or use indicators of compromise can be improved, closing cybersecurity gaps by managing and interpreting behavioral indicators.

Vulnerability Management: The number of vulnerabilities is growing yearly, and it is not enough to wait for cybercriminals to exploit them before reacting to them. User and Event Behavioral Analytics (UEBA) makes it possible to identify anomalous behaviors that indicate cyberattack activity even before patches that correct vulnerabilities are available.

Data Centers: AI, as in other areas in which it operates, facilitates the optimization and monitoring of essential data processing centers and helps detect threats of anomalous behavior. It improves the use of these resources and their evolution, with the consequent cost savings and risk reduction, such as service downtime or the execution of malicious software.

Network Security: Artificial Intelligence makes it possible to learn the traffic behavior patterns in networks both in the field of action policies against user behavior and in the more topographic field when it comes to identifying which processes correspond to each application. AI can recommend workload grouping and the application of security policies.

Securing Authentication: Both for the protection of users accessing our services and the set of elements they use, Artificial Intelligence can identify the use of false identities or brute force attacks, providing an additional barrier to fraudulent access to our services beyond user authentication or the use of captcha.

Information Privacy and Compliance: Artificial Intelligence helps to classify information automatically by its level of criticality in the face of different regulations such as GDPR. This implies savings compared to the efforts currently made manually, avoiding the risks that this entails.

Blocking of Bots Based on Their Behavior: The activity of bots, without being malicious, consumes the bandwidth of our servers, harming the user experience of our real clients. Artificial Intelligence allows classifying the activity of these visitors to limit their actions.

These use cases have become a reality for leading players in the digital world, such as Google, which has integrated Artificial Intelligence in cybersecurity within its Gmail service, and IBM/Watson, which includes this combination among its security tools. Other notable examples include Juniper Networks and Balbix.

Due to cybercriminals increasingly utilizing Artificial Intelligence for their attacks and the benefits it offers in managing various cybersecurity systems and services, implementing security solutions that leverage AI has become essential. As a result, we will become more adept at detecting hackers, reducing costs at current security levels, and enhancing the experience for our customers and users.