Today, developments of AI have gone far, and it is having a tremendous impact in areas such as content generation, cybersecurity, and business process automation.
As companies like OpenAI with ChatGPT and Meta’s open-source Llama are democratizing AI, some threat actors have started exploiting these innovations, leading to cheaper and more sophisticated cyber attacks. The misuse of these technologies presents a significant risk, as threat actors can now easily exploit vulnerabilities in systems, posing challenges for security teams.
Let’s dive deeper into what AI can be, how it operates, and how it can be applied in cybersecurity to protect organizations’ and customers’ confidential data from unauthorized access and data breaches.
What Is AI?
Artificial Intelligence (AI) is a technology that enables machines to emulate thinking to perform tasks that require human intelligence, such as learning, decision-making, and problem-solving, etc.
Types of AI
Generative AI. This type of AI creates new data – text, images, music. Examples include OpenAI GPT-4 and Midjourney. These systems use deep neural networks to create content based on given data and context, opening up opportunities to automate creative and analytical processes.
Distributed AI. Systems that work in a coordinated manner to solve complex problems are known as Swarm AI. This approach is often used in networked systems to manage cybersecurity and distributed computing networks.
Specialized AI. Used in cybersecurity systems today, such as facial recognition and automating log analysis, this AI tackles narrow, specific tasks.
AI technologies
AI encompasses a wide range of technologies, approaches, and architectures, each of which plays a specific role in improving findings, be it data analysis, code generation, or automation.
Machine Learning (ML)
Machine Learning is a fundamental AI technology that allows systems to learn from data and make predictions without the need for explicit programming. ML uses algorithms that process input data and learn from errors, allowing the model’s performance to improve over time. There are several types of machine learning, including supervised learning, where the model is trained on labeled data, and unsupervised learning, where the model must find patterns on its own.
Deep Learning (DL)
Deep learning is a machine learning technique based on the use of multi-layer neural networks. Each neural network consists of multiple layers where data passes through each layer, learning to find connections and patterns. The importance of deep learning lies in its ability to analyze complex, high-dimensional data such as images, sounds, or large amounts of text. Because of its architecture, deep learning is able to solve problems that require recognizing complex structures in data, such as image classification, text translation, or time series prediction.
Recurrent Neural Networks (RNNs)
RNNs are a type of neural network that is particularly effective for dealing with sequential data such as text or time series. The main difference between RNNs and other neural networks is that they have a “memory” that allows them to take previous elements of a sequence into account when processing the current element.
These are ideal for handling sequential data like time series and visual data, essential for endpoint security and privileged sessions monitoring. However, RNNs have limitations, such as problems with learning on long sequences, which have led to the development of new technologies such as transformers.
Convolutional Neural Networks (CNNs)
CNNs are neural networks designed to analyze visual data that use convolutional filters to extract key features such as edges, textures, and objects. These filters scan the image in parts, extracting important information at different levels.
CNNs are widely used in computer vision tasks such as image, object, and face recognition due to their ability to efficiently handle high dimensional data, significantly reducing the number of parameters required.
Transformers
A modern neural network architecture is used to deal with sequences of data, enabling tasks like content generation and analysis and threat detection. Unlike recurrent neural networks (RNNs), Transformers are able to process the entire input data set simultaneously, making them more efficient for prediction and generation tasks.
Transformers utilize attention mechanisms that help models focus on the most important parts of the data. It is this technology that has become the basis for many modern language models such as GPT and BERT, which are able to understand context and generate meaningful text.
Natural Language Processing (NLP)
NLP is an area of AI that enables machines to understand, interpret, and generate human language. NLP includes several key technologies such as syntax, semantics, and linguistic pattern analysis. NLP is used to perform tasks such as translating text, automatic summarization, answering questions, and analyzing the tone of text.
The application of NLP is made possible by technologies such as transformers and machine learning, which allow models to understand the context and meaning of text rather than just performing superficial analysis.
Autoencoders
Autoencoders are a type of neural network that is trained to compress (encode) data into a more compact representation and then reconstruct (decode) it back to its original form. This is done in order to identify hidden patterns in the data or to perform information compression tasks.
Autoencoders are widely used for data dimensionality reduction tasks, noise removal, or generative modeling. They are useful in tasks where you need to work with large amounts of data while preserving key information.
AI in Cybersecurity and Cyber Threats
AI is actively used for data protection, threat detection, and attack prevention. By leveraging AI, organizations can enhance their security posture by identifying vulnerabilities in critical infrastructure and taking proactive measures. AI-driven systems can analyze vast amounts of data, detect anomalies, and predict possible threats, enabling comprehensive security and effective access management.
AI and Access Control
AI can analyze user behavior and detect anomalies in real-time, significantly improving access control systems and ensuring that only the necessary levels of access are granted. This reduces the likelihood of attackers gaining unauthorized access through stolen credentials. AI-enhanced user lifecycle management is also crucial in modern Identity and Access Management (IAM) solutions, enabling better governance of privileged access and service accounts. This governance ensures that all end point users, whether they use mobile devices or traditional IT workstations, comply with strict audit and compliance requirements.
AI and Data Security
AI models can help identify potential threats to sensitive data by analyzing user behavior and automatically responding to incidents, helping to prevent data breaches. This is especially important in environments that involve smart devices and cloud environments, where constant monitoring of data and applications is required. AI helps protect these secure systems by automating processes that safeguard against unauthorized access and credential theft.
AI in Cloud Security
With the rise of cloud environments, maintaining secure systems requires continuous monitoring and advanced protection. AI plays a key role in identifying anomalies within network security and cloud transactions, ensuring the safety of both data and applications running in the cloud. Organizations benefit from AI’s ability to handle the complexities of securing data centers, enabling them to reduce operational costs while maintaining high levels of protection.
AI and Network Security
AI can analyze network traffic, identify potential threats, and respond in real-time. This makes AI a powerful tool for safeguarding enterprise networks from cyberattacks. It also helps system administrators ensure that service accounts and privileged user access remain secure, further strengthening the organization’s security posture.
Challenges of using AI in cybersecurity
AI can significantly improve data and infrastructure protection, but its use in cybersecurity comes with a number of serious challenges that must be considered for successful implementation and operation.
The Need for Large Amounts of Data
AI systems require access to large amounts of data to effectively train and operate. AI analyzes patterns, anomalies, and risks based on statistical processing of data, which allows it to “learn” and become more accurate in predictions. However, if a company does not have sufficient volumes of quality data, AI’s performance can be severely limited.
The problem here is that without a rich and diverse data set, AI systems can become biased, making errors when processing information. This can also lead to misclassification of threats and false alarms, weakening overall defenses.
Additionally, companies that collect and use large amounts of data face challenges in storing and processing that data, especially if their systems are not optimized to handle large data sets. This requires significant financial and technical investment in infrastructure.
False Positives
One of the primary risks in using AI for cybersecurity is the potential for false positives, where legitimate actions are mistakenly flagged as threats. These false alarms can place undue stress on security teams, who must manually verify each alert. Such occurrences often stem from improperly trained AI models or biased data sets. As a result, this can lead to alert fatigue, where crucial security risks are overlooked, leaving the organization vulnerable to actual threats.
False positives also impact access management by inadvertently restricting authorized users or service accounts, causing disruptions to business operations. Proper tuning of AI models is critical to ensure only the necessary levels of access control are enforced without triggering unnecessary alerts.
Limited Computing Resources
AI systems require significant computing power to process data and perform complex tasks such as analyzing user behavior or predicting potential attacks. For companies, especially SMEs, this can be a major barrier to AI adoption.
The operations involved in processing large amounts of real-time data can put a heavy strain on IT infrastructure. This is especially important for companies that operate large distributed systems where every transaction must be analyzed for anomalies.
In addition, maintaining and updating AI models requires constant access to powerful servers and specialized data processing solutions, which can require additional hardware and software investments.
AI Licensing and Сontrol Issues
Organizations using third-party AI solutions may encounter licensing issues, which can restrict their ability to modify or adapt algorithms to meet specific audit and compliance requirements. Such licensing limitations can also hinder the integration of AI with existing security frameworks, complicating efforts to ensure comprehensive security and limiting the flexibility needed to respond to evolving cyber threats.
Complexity of Adapting to Regulations
One of the key challenges of implementing AI in cybersecurity is the need to comply with regulatory requirements such as GDPR (General Data Protection Regulation), NIS2 (Directive on Security of Network and Information Systems), and other industry standards.
AI systems often handle large amounts of personal and sensitive data, which imposes additional requirements for their protection and management. For example, GDPR requires strict controls over the collection, storage, and processing of personal data, as well as transparency in the use of that data. Companies using AI must ensure that the data they use to train AI complies with these requirements. This includes:
- Data Anonymization. Data used by AI should be anonymized or depersonalized to prevent leakage of sensitive information.
- Data traceability. Companies should be prepared to provide a full account of how and for what purposes data was used, especially if it involves personal information.
- Consent to data processing. AI can be useful for analyzing user behavior, but it requires consent from data subjects, which creates additional compliance challenges.
In addition, directives like NIS2 require companies to ensure infrastructure resilience, including access controls, and threat and incident protection, where AI can play a key role.
The Role of AI in Privileged Access Management
Artificial intelligence (AI) is revolutionizing the way organizations manage and secure privileged access. By leveraging AI, PAM solutions can significantly enhance security and operational efficiency. AI-powered PAM systems are capable of analyzing user behavior, detecting anomalies, and identifying potential security threats in real-time and respond swiftly to potential security breaches, thereby preventing unauthorized access to sensitive data.
By integrating AI into PAM, organizations can achieve a higher level of security and efficiency simultaneously, ensuring that sensitive data remains protected from unauthorized access and sophisticated threats.
Benefits of AI-Powered Privileged Access Management
The integration of AI into privileged access management (PAM) brings a multitude of benefits that enhance both security and operational efficiency. Here are some of the key advantages:
- Improved Efficiency. AI-powered PAM solutions can detect and respond to potential security threats in real-time by continuously monitoring user activity, identifying anomalies that may indicate unauthorized access attempts, and taking measures before data breaches occur.
- Enhanced Visibility. AI-powered PAM solutions provide deep insights into user behavior and access patterns. This enhanced visibility allows organizations to identify and refine their security policies precisely and take proactive steps to mitigate risks.
- Reduced Risk. AI-powered PAM solutions are adept at identifying potential security risks and providing adaptive actions for their mitigation, helping organizations reduce the risk of data breaches and more sophisticated cyber threats, ensuring that sensitive data remains secure.
By leveraging AI in PAM, organizations can achieve a more robust security posture, reduce the risk of unauthorized access, and improve overall operational efficiency.
How Fudo Implements AI in Privileged Access Management to Overcome Challenges
Fudo Security is a European company, and we build all our solutions considering strict UE regulations. Our Intelligent PAM uses a proprietary AI model developed from scratch. This means that we have full control over the development process and because of this, the AI model can meet the specific requirements of customers, regulators, and infrastructure features. Additionally, securing operating systems like Linux and Windows against unauthorized access is crucial, and our AI model is designed to enhance this security.
The same about our PAM solutions overall, that built on FreeBSD, making it entirely the most secure and reliable foundation for development, and frees up to customize and adjust it without any restrictions. And, our solutions are on-premise, which means you store data on your side and have full control over everything that is going on in your systems.
AI-Privileged Session Monitoring and Recording
Our AI analyzes user behavior by detecting anomalies based on biometrics (e.g., keystrokes and mouse movements) to help prevent account compromise proactively.
Behavioral and Semantic Analysis
AI Fudo monitors behavioral patterns and the context of user interactions with systems to identify deviations from normal behavior, prove that the assigned person uses its account, and block suspicious activities in real time.
False Positive Analysis and Model Training
One of AI Fudo’s key objectives is to minimize false positives. The model is continuously trained on user data and uses biometric analytics (keyboard, muscle, and semantic biometrics) to reduce false alarms and automatically improve its algorithms. Moreover, you can choose the level of accuracy, so the time needed for model training considering your organization’s features and goals.
Automated Incident Response
Fudo’s AI tools are integrated into the incident management system. When the model detects risky behavior, it can automatically pause a session or terminate it as needed based on threat probability thresholds (e.g., 25%, 50%, 80%) and policies you choose. The data is sent to your SIEM/SOC for further analysis and action.
How Does Fudo AI-Powered PAM Enhance Your Security Operations?
Technically
Fudo AI utilizes advanced technology to monitor, manage, and secure privileged sessions in real time. Our Intelligent PAM solution allows you to customize and automate access control and respond to incidents with minimal human intervention, with user behavior analysis, and blocking anomalies before they escalate into a threat.
Operational
Fudo PAM can be deployed in 24 hours, significantly reducing implementation time. Unlike competitors, our solution does not require large-scale resources, requiring you to slow down your operations for weeks or even months for complete deployment and integration.
Our NextGen PAM solutions support rapid and flexible integration with various platforms, providing many connection protocols and authentication systems that will seamlessly complement any part of your infrastructure.
Regulatory Compliance
Our solution is designed taking into account international and European regulatory standards NIS2, ISO27001, GDPR, and others, to help the companies meet their strict requirements by having the best access management and remote access controls in place.
That’s why we use FreeBSD as a foundation, developed from scratch the best rewarded AI model in the market, and implemented core security principles such as Zero Trust, Least Privilege, Just-in-Time, and more.
Conclusion
With Fudo AI, companies can reduce the risk of cyberattacks and data breaches, increase productivity, and minimize the cost of security management. Our solution has been recognized as the best in the market in a few categories and obtained critical EU certifications, as evidenced by prestigious awards and high praise from our customers.
Contact us to get more info on how Fudo AI-powered NextGen PAM solutions can empower your organization, and request a free quote for a demo to get the perfect mix of technology advancement and business efficiency.
