Generative AI and ChatGPT can help build a robust security strategy by providing information on the most vulnerable endpoints and identifying risk timelines.
A few years ago, AI was first utilized to address the use cases of malware detection and anomaly detection in the cybersecurity industry. Businesses have made significant progress in understanding the benefits and restrictions of using AI in cybersecurity, especially in the zero-trust era. AI is still necessary even in a zero-trust architecture. Zero trust requires more AI, even though it reduces the possibility of an anomaly occurring and reduces the attack surface.
Today, organizations require a customized, contextual, dynamic, and granular security policy in the zero-trust era, which is exactly what zero trust is about. For instance, access control now relies on a complex set of policies based on identity, device, posture, intention, risks, content, and rich data points rather than a set of simple rules.
However, using human rules and heuristics to generate such complex, detailed, and personalized policy at scale can be time-consuming. Different employees will use various applications, and the way these applications are used may need to change quickly over time. Making such an intelligent and individualized security policy recommendation at scale requires AI.
AI may make suboptimal recommendations in the eyes of experts because AI cannot capture or comprehend all the subtleties and contexts of any complex environment. Organizations can enhance the AI model’s performance with ongoing human input.
Using Good AI for ensuring Zero-trust security strategy
Threat actors equipped with artificial intelligence (AI) tools like ChatGPT can succeed in AP biology, the bar exam, and even the creation of polymorphic malware. The cybersecurity community is responsible for using AI to combat it. Identity management is the first step toward using AI for critical cybersecurity applications beyond basic language learning. There is no way to achieve zero trust without AI. When it comes to attack vectors, identity is where the action is.
The security operations center (SOC) is overworked, which is not exactly a big secret. The industry standard for identifying and resolving an attack is 277 days. However, AI makes it possible to control access in the most specific terms, in real-time, and at the data level, resulting in a framework based on the principle of least privilege.
The role of humans will change to include developing algorithms to address crucial issues, monitoring AI activity, and handling exceptions. Humans are the ones who will pose the previously unasked questions. Humans and businesses will eventually need AI to keep them safe. Additionally, ‘good’ AI can be protected by the cybersecurity community.
Developing Risk-Based Security Strategies
A risk-driven approach is replacing the traditional compliance-focused security approach by security leaders. The distinction is that a risk-based approach continuously assesses the threat landscape and takes preventative action to mitigate threats, unlike a traditional system where an organization reacts to general compliance requirements.
Organizations can use machine learning technology to assess real-time user requests, evaluate the security context, including the device, network, and related behavioral data, and produce a risk score. This dynamic risk score can be used in access control policies to grant access, deny access, or demand more rigorous authentication.
There may be thousands of users, hundreds of cloud services, and large organizations. In this complex environment, there is a need to enforce standardized policies and reliably identify violations consistently.
Machine learning can be useful by automatically modifying access policies in response to real-time behavioral pattern analysis. This can be done automatically based on the organization’s centralized policies rather than requiring IT and security teams to review access requests and constantly grant access. This also offers a better experience for the user because they get quick access without having to wait for manual approvals if their request is valid.
Enhancing User Experience
In the past, there was a trade-off between enhanced security and a satisfying user experience. The use of more secure systems was frequently frustrating and challenging. Organizations now recognize that security should be transparent to users, and unobtrusive; users will adopt certain authentication mechanisms, increasing safety.
Users and staff disliked early iterations of multi-factor authentication (MFA) because they negatively affected user experience. Many organizations opted to make MFA optional or not adopt it to prevent scaring off users. The latest in zero trust technology is, however, altering that.
A more streamlined user experience with modern authentication that uses machine learning techniques and considers the user’s security context may be possible. One example of a passwordless authentication system is a system that uses a combination of dynamic authentication factors to authenticate a user. Another illustration is that when users use their corporate workstations to log in from home, they might log in using SSO authentication tokens without entering a password. In contrast, the authentication system can indicate that a user’s attempt to log in remotely or at an odd time requires additional authentication.
Users will support this differential authentication because it makes sense and improves their daily login experience. Differential authentication based on machine learning risk scoring can make users collaborators in a company’s security initiatives.
Next-Generation Antivirus (NGAV) with AI
By identifying new attacks that do not match a recognized file signature, NGAV outperforms legacy antivirus. It can defend against file less attacks, zero-day attacks, and malware that modifies its source code to evade detection by conventional antivirus software.
To identify unknown threats, NGAV employs a variety of machine-learning techniques:
- Using static features, comparing specific binary or assembly code fragments to known malware code segments.
- Analysis of printable strings found in executable program output, such as URLs, file paths, menus, API commands, configuration files, and command line output, is known as string analysis.
- N-gram analysis is the study of byte sequences taken from allegedly malicious binaries.
- Entropy is the statistical variance of code that can be used to spot encoded or obfuscated code.
- Visualizing binary content entails converting malicious software’s binary code into an image format, where each byte corresponds to a pixel, and then performing computer vision analysis on the resulting file.
- Using a control flow diagram to contrast suspected malware with known malware is known as a control flow graph (CFG).
Artificial intelligence (AI) in eXtended Detection and Response (XDR)
To identify evasive threats, XDR is a new class of security technologies that correlates and analyzes data from various sources. A zero-trust environment enables detection and proactive hunting for advanced threats. XDR solutions use machine learning techniques to piece together data points from networks, endpoints, and cloud logs to detect attacks and facilitate easier investigation. High dimensions characterize this data. Combination attack data analysis uses:
Data pre-processing that is automated: XDR solutions collect data from various sources, normalize it, and clean it automatically so that machine learning algorithms can use it. XDR solutions, which extract real-time data from IT systems and transform it into standardized datasets that can be fed to unsupervised and supervised ML algorithms, are essentially a data scientist in a box.
Unsupervised machine learning: XDR solutions employ unsupervised algorithms to build baseline user and device behavior, identify groups of peers, and ascertain typical behavior for a particular entity. Then, it can detect malware, lateral movement, command and control (C&C) communications, and data exfiltration by comparing past, present, and peer behavior.
XDR solutions use supervised machine learning to identify various types of entities on a network, including Windows computers, email servers, and Android phones. Then, using known suspicious events as a training set, it can train large-scale supervised algorithms on traffic and events associated with a group of entities. After that, the algorithm uses inference to spot suspicious events live. False positives can be significantly decreased using this strategy.
Also Read: Challenges of 5G for Cybersecurity
Large-scale micro-segmentation projects may take months or even years due to the numerous difficulties in performing micro-segmentation correctly. Although network micro-segmentation aims to separate and isolate specific network segments within an organization, this is rarely a one-and-done task.
By figuring out the best way to introduce micro-segmentation without obstructing access to systems and resources, generative AI can be of assistance. The best part is that it might prevent thousands of IT service management systems trouble tickets from being generated by a failed micro-segmentation project.
Resolving the security challenge of managing and protecting endpoints and identities
Attackers look for weaknesses in identity management and endpoint security. By providing threat hunters with the information they need to know which endpoints are most at risk of a breach, generative AI and ChatGPT can help address this issue.
Generative AI could be used to continuously learn how, where, and by which methods attackers are trying to penetrate an endpoint and the identities they’re trying to use, in line with the need to improve muscle memory, especially regarding endpoints.
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