security

The Advantages of a Passwordless Authentication Within a Zero Trust Security framework

The rapid shift towards more remote working and the associated explosion of devices has dramatically increased the number of cyber threats. With this in mind, companies face the challenge of protecting their highly complex cloud-based technology ecosystems, as employees, software and even partner organisations can pose a threat to the security of valuable systems and data. As a consequence, the zero-trust approach has established itself as a popular security framework. What is Zero Trust? In a Zero Trust architecture, the inherent trust in the network is removed. Instead, the network is classified as hostile and every access request is checked based on an access policy. An effective zero trust framework combines several tools and strategies and is based on one golden rule: trust no one. Instead, each entity (person, device or software module) and each access request to technology resources must provide enough information to earn that trust. If access is granted, it applies only to the specific asset needed to perform a task and only for a limited period of time. The role of zero-trust authentication Because password-based, traditional multi-factor authentication (MFA) can be easily exploited by cybercriminals, an effective zero-trust approach requires strong user validation through phishing-resistant, passwordless MFA. It also requires establishing trust in the endpoint device used to access applications and data. If organisations cannot trust the user or their device, all other components of a zero-trust approach are useless. Authentication is therefore critical to a successful zero-trust architecture, as it prevents unauthorised access to data and services and makes access control enforcement as granular as possible. In practice, this authentication must be as smooth and user-friendly as possible so that users do not bypass it or bombard the helpdesk with support requests. The advantages of passwordless authentication Replacing traditional MFA with strong, passwordless authentication methods allows security teams to build the first layer of their zero-trust architecture. Replacing passwords with FIDO-based passkeys that use asymmetric cryptography, and combining them with secure device-based biometrics, creates a phishing-resistant MFA approach. Users are authenticated by proving that they own the registered device, which is cryptographically bound to their identity, through a combination of biometric authentication and asymmetric cryptographic transaction. The same technology is used in Transaction Layer Security (TLS), which ensures the authenticity of a website and establishes an encrypted tunnel before users exchange sensitive information, for example in online banking. This strong authentication method not only provides significant protection against cyber attacks, but can also reduce the costs and administrative tasks associated with resetting and locking passwords with traditional MFA tools. Most importantly, there are long-term benefits through improved workflow and staff productivity, as authentication is designed to be particularly user-friendly and frictionless. Zero trust authentication requirements at a glance It is important that organisations looking to implement a zero trust framework address authentication as early as possible. In doing so, they should pay attention to the following points: 1. Strong user validation A strong factor to confirm the identity of the user is the proof of ownership of their assigned device. This is provided when the authorised user verifiably authenticates himself on his own device. The identity of the device is cryptographically bound to the identity of the user for this purpose. These two factors eliminate passwords or other cryptographic secrets that cybercriminals can retrieve from a device, intercept over a network or elicit from users through social engineering. 2. Strong device validation With strong device validation, organisations prevent the use of unauthorised BYOD devices by only granting access to known, trusted devices. The validation process verifies that the device is bound to the user and meets the necessary security and compliance requirements. 3. User-friendly authentication for users and administrators. Passwords and traditional MFA are time-consuming and impact productivity. Passwordless authentication is easy to deploy and manage and verifies users within seconds via a biometric scanner on their device. 4. Integration with IT management and security tools Collecting as much information as possible about users, devices and transactions is very helpful in deciding whether to grant access. A zero-trust policy engine requires the integration of data sources and other software tools to make correct decisions, send alerts to the SOC and share trusted log data for auditing purposes. 5. Advanced policy engines Deploying a policy engine with an easy-to-use interface enables security teams to define policies such as risk levels and risk scores that control access. Automated policy engines help collect data from tens of thousands of devices, including multiple devices from both internal employees and external service providers. Because using risk scores instead of raw data is useful in many situations, the engine also needs to access data from a range of IT management and security tools. Once collected, the policy engine evaluates the data and takes the action specified in the policy, for example, approving or blocking access or quarantining a suspicious device. Traditional password-based multi-factor authentication is now a very low barrier for attackers. An authentication process that is both phishing-resistant and passwordless is therefore a key component of a zero-trust framework. This not only significantly reduces cybersecurity risks, but also improves employee productivity and IT team efficiency.

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How Artificial Intelligence Helps Minimizing Cyber Risks

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.

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DKB Customer Success Story: IAM Tool Implementation and Segregation of Duties

Do you enjoy reading customer success stories? If yes, download PATECCO latest whitepaper. It describes how a renowned German banking institution overcomes a number of security challenges by means of unique combination of strategies, methods, and integration of an IAM tool, coupled with robust segregation of duties practices. This customer success story serves as a good example and as an inspiration for the financial companies to be more active, to be alert and to be more responsible in providing security, efficiency, and compliance in the dynamic landscape of the banking industry. Click on the image and download the document:

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