artificial intelligence

How to protect digital identities in the era of AI?

Making online transactions increasingly secure, despite the rise in cyberattacks and data theft, has been a growing challenge for our economy since the pandemic. More and more companies feel that their existence is threatened by cyber-attacks. Identity fraud and other online threats are also becoming increasingly sophisticated. According to a recent study, digital identities pose one of the greatest threats to the compromise of IT systems. In the financial sector for example, numerous attacks have recently been successful because employees with privileged access rights have been spied on and digital identities have been stolen through phishing campaigns. Therefore there is an urgent need to better protect our identities from theft and damage in the digital age. After all, our identity is the centrepiece of our online lives. Data leaks, malware or insecure networks and connections are a gateway to identity theft. AI will further accelerate this by generating code that only experienced hackers could create. AI-generated forgeries will become increasingly authentic and easy to use against victims. Identity protection will become one of the most important elements of data security. To better protect themselves in a digitally hostile environment in the future, organisations should push for a digital approach to data protection and risk management. What does identity involve and how we can protect it? Our online identity consists of several layers. On the one hand, there is our personal data that we use to verify ourselves, such as email, place of birth, date of birth and name. But it also includes things like bank details and other tokens – even biometric data. The following basic steps help protect identities from AI-powered risks. These are principles that, if followed, can provide identity protection for individuals, as well as legal obligations that companies must fulfil to mitigate risk and protect both customers and employees: The online identity check at a glance There are various ways in which users can have their identity verified online. This can be done through the use of facial recognition tools, video calls and the presentation of personal information. The use of multiple forms of proof increases the level of trust and security. For example, linking transactions to a one-off secure onboarding can provide a high level of trust – as long as this onboarding is thorough and verified. One example: passports. Official auditors rely on a few measures to verify them, but they trust them because the onboarding process for securing a passport is rigorous. Real-time ID verification: It enables organisations to access and process customer information in seconds. This is particularly useful when checking IDs or financial transactions, as it can ensure that a company has up-to-date information. Verifying the identity of signatories is important to help organisations reduce fraud and money laundering and provide a frictionless experience for signatories, as today a fast and mobile-friendly online process is crucial for customers. Biometrics: These are fingerprint or facial recognition. Biometric technology is the frontrunner among identity verification tools and is used, for example, to unlock smartphones. As biometric technologies have already proven to be beneficial for personal security and ID verification, it stands to reason that they will also provide a secure way to verify electronic signatures in the near future. They also assure companies that no unauthorised person can gain access to personal data. Verification of identity without ID: There are various alternatives for authenticating a signature without the need for ID. If the signatory does not have an ID card to hand, knowledge-based verification can be used. Alternatively, authentication via mobile phone will be used more frequently in future to re-authenticate a known user. This involves sending an access code or text message to a mobile phone number or using the phone itself as an authentication vector and ID. Reducing fraud with AI While AI can increase the scope and scale of cyber risks, it also plays an important role in risk mitigation: in the future, AI will support identification during initial onboarding and all subsequent steps by providing a layer of assurance that looks for evidence of trust or signs of deception. It is important that customers perceive a company’s digital signature process as secure, as a positive and secure customer experience will influence whether they want to continue using the company’s services. There are also many use cases where AI is already being used to detect fraudulent activity – such as detecting relevant signals or patterns of unlikely user behaviour on a platform based on previous experience. However, the ideal scenario is to utilise both humans and AI to increase security and trust. Both can bring risks, but together they help to optimise resilience to cyber-attacks and strengthen identity protection. To summarise, simple changes to processes, a clear understanding of digital identity protection and regular employee training on cyber security and data protection can often lead to significant improvements in corporate security.

What is the Influence of AI and ML on Privileged Access Management?

Uncategorized / Von Ina Nikolova

Artificial intelligence and machine learning are now influencing almost all industries and work processes. The positive impact on the productivity and efficiency of work processes is offset by the increase in the number and threat level of cyber attacks: security vulnerabilities can be detected more easily and exploited in a more sophisticated way thanks to the new methods. In view of the shortage of IT security specialists, the use of AI and machine learning also creates advantages for overcoming precisely this challenge. In the early days, the concept of managing privileged access was extremely simple. A few selected IT administrators were given the „keys“ to access critical systems and data. Today, the number of privileged users has increased exponentially as the digital transformation progresses. It is no longer just IT administrators who hold these „keys“, but also company employees or third-party providers, for example, who need access to sensitive systems and data for very different reasons. This expansion of the user side has significantly complicated the security landscape, making traditional Privileged Access Management solutions less effective. The misuse of privileged access – whether deliberate or accidental – is just one challenge that companies face. There is also a growing need for proof of privileged user credentials, as regulators are increasingly demanding them. Companies therefore need advanced PAM solutions that adapt to the digital landscape, detect threats in real time and respond to them to provide a sufficient level of security. This is where Artificial Intelligence (AI) and Machine Learning (ML) come into play. By harnessing AI and ML, companies can improve their security posture, reduce the risk of security breaches and ensure regulatory compliance. How PAM technologies utilize the advantages of artificial intelligence? AI and ML can analyze and learn from the login behavior of privileged users. By understanding what normal behavior looks like, these technologies can detect anomalies that could indicate a security risk. For example, if a user who normally logs in during normal business hours suddenly logs in late at night, this action can be classified as suspicious. The same applies to the login location. If a user who normally logs in from a specific location suddenly does so from a location, this can also be flagged automatically and indicate that the corresponding login data has been compromised. AI-powered PAM solutions effectively track user behavior and quickly flag any deviation from regular patterns. This feature provides deeper insight into user behavior and enables proactive and more effective threat detection and response. Perhaps one of the most powerful applications of AI and ML in PAM is their ability to predict anomalies. By analyzing historical data and identifying patterns, these technologies can predict potential security threats before they occur, allowing organizations to take proactive measures to mitigate them. Effective PAM solutions use AI to analyze enterprise data and provide security professionals with insightful data as they make access decisions. This capability enables real-time monitoring of evolving threats, attack patterns and risky behavior, allowing organizations to respond quickly and effectively to potential security threats. Privilege elevation and delegation are key aspects of Privileged Access Management (PAM) that involve managing and granting elevated permissions to users for specific tasks while minimizing the risk associated with such privileges. Artificial Intelligence can play a crucial role in optimizing and securing privilege elevation and delegation processes within a PAM framework. AI can be applied in areas such as Contextual Authorization, Automated Workflow and Approval, Role Mining and Entitlement Management, Privilege Delegation Recommendations and Audit Trial analysis. An efficient PAM solution should also provide risk scoring regarding individual users based on their behavior and historical data. This feature enables administrators to make informed decisions about granting or revoking privileged access and thus improve the organization’s security posture. Real-time analysis of access requests enables adaptive management decisions that are not just based on fixed rules. This feature enables a more dynamic and responsive PAM approach and ensures that the organization’s security measures keep pace with the evolving threat landscape. The benefits listed above clearly show that the use of AI and machine learning for IT security is no longer an option, but a necessity. These technologies offer promising opportunities to improve the efficiency of PAM solutions and thus strengthen the level of security in organizations. By using these technologies, companies can improve their security posture, reduce the risk of security breaches and improve compliance with legal requirements. AI can integrate with threat intelligence feeds to enhance PAM solutions‘ ability to recognize and respond to emerging threats. When integrated with AI-driven PAM solutions, threat intelligence contributes to a more robust security framework and helps PAM systems stay updated on the latest security threats and vulnerabilities. When we talk about Risk Assessment and Prioritization AI can analyze threat intelligence data to assess the risk associated with various activities and access requests within the organization. By combining threat intelligence insights with behavioral analytics, AI can prioritize and assign risk scores to different access attempts, helping organizations focus on addressing the most critical threats first. Threat intelligence feeds provide information about the latest cyber threats, vulnerabilities, and attack techniques. AI algorithms can process this information in real-time, allowing PAM solutions to proactively detect and respond to emerging threats before they can be exploited. In a nutshell, the integration of artificial intelligence and machine learning into Privileged Access Management enhances security by providing advanced analytics, automation, and adaptive responses. This results in a more resilient and responsive security framework, crucial for safeguarding privileged access to sensitive systems and data in today’s complex cybersecurity landscape.

How Artificial Intelligence Helps Minimizing Cyber Risks

Uncategorized / Von Ina Nikolova

The digital age has opened up numerous opportunities for us, but at the same time we are exposed to entirely new cyber threats. Never before we have been as connected as we are today – across all sectors and areas of life, in industry, business and society. Especially through the Internet of Things and artificial intelligence, processes are becoming more and more automated and optimized. The challenge for cybersecurity is that every exchange of data must be secured and protected from unauthorized access. Furthermore, cybercriminals are constantly looking for ways to compromise networks and steal sensitive data. These techniques are becoming increasingly advanced and can be difficult to detect by humans or traditional defense solutions. For this reason, organizations are looking to AI techniques to strengthen their cybersecurity defense plan. Artificial intelligence in cybersecurity can help companies understand and defend against these threats. How can companies protect themselves against cyber risks? As already mentioned, the application of AI has significantly impacted people’s lives. We now have machines that can drive cars, understand verbal commands, distinguish images, and play games. This is the reason why AI and machine learning have become indispensable to information security, as these technologies are able to quickly analyze millions of data sets and detect a wide range of cyber threats – from malware threats to phishing attacks, ransomware and zero-day vulnerabilities. These technologies are constantly learning, using data from past cyberattacks to identify potential threats. Regarding IT security, companies must ensure that they develop and operate a holistic security concept. In addition to using the appropriate protection products such as firewalls, virus protection or backups, this also includes active management of the IT components. All network components must not only be permanently patched and updated, but also continuously monitored. This ensures that security gaps are detected as quickly as possible. IT monitoring tools can be used not only to continuously monitor networks, servers, applications and other IT components to ensure that they are functioning properly, but to measure the performance of IT systems and detect security incidents, as well. Active monitoring is usually difficult for companies to implement, which is why support from a managed service provider is advisable. AI for cybersecurity can help you detect threats masquerading as normal traffic, and can process and analyze a large amount of data more thoroughly and in less time. A managed service is responsible for the provision and management of a company’s IT infrastructure. In doing so, we ensure that the customer’s IT infrastructure is always available and functional. Integrated services such as update management and monitoring, significantly increase the IT security. Of course the MSP use special software and AI-supported tools to ensure that potential attackers do not take advantage of artificial intelligence. Proper vulnerability management is the best way to secure an organization’s network. As mentioned earlier, a lot of traffic flows through an organization’s network, and it is imperative to detect, identify, and protect that traffic from malicious access. Unlike human security personnel, AI can quickly learn network behavior to identify vulnerabilities in the system, allowing organizations to focus on ways to mitigate those risks. In this way, vulnerability management can be improved and the enterprise can secure its network systems in a timely manner. Given the speed at which cyber threats evolve, it’s a fact that traditional rules-based security systems can’t keep up. This is where AI systems come into play. AI technologies are equipped with advanced algorithms that detect malware activity, perform pattern recognition and identify anomalous behavior before the system is compromised. Machine learning algorithms can learn from historical data and behavior patterns to identify new and emerging threats, including malware, ransomware, and phishing attacks. AI systems can help identify your IT inventory, a documented record of all tangible and intangible assets. Cybercriminals are always trying to target these assets. Using AI in cybersecurity, you can predict how and when a cyberattack will occur and plan accordingly to allocate resources to the most vulnerable areas. One of the key benefits of incident response automation is its ability to significantly reduce the time it takes to detect, respond to security threats and remediate security incidents. AI and ML-powered tools can monitor network traffic, user behavior, and system logs to detect unusual activities that may indicate a cyberattack. This allows organizations to identify potential threats much more quickly than would be possible using manual methods, enabling them to take action before any significant damage is done. Cyberattacks are becoming more advanced, and cybercriminals are finding more creative ways to carry out their evil plans. That’s why companies are turning to AI to strengthen their defenses and mitigate cyber risks. AI offers so many cybersecurity benefits, including vulnerability management, risk prediction, threat detection, and network traffic monitoring. We hope this article has given you some insight into the use of AI in cybersecurity.