Zero-Trust Log File Intelligence: What You Need to Know
Zero-trust access is a rigorous security model that is increasingly becoming the benchmark for businesses and governments. It’s a departure from traditional perimeter-based security, where the identity and authorization of users and devices is constantly challenged and verified before access is granted – even to the CEO, who has worked there for 20 years. Users are then given only the minimum permissions necessary to perform their tasks, limiting the potential damage they can cause while ensuring they can still do their work.
One area where zero-trust can be effective is log file intelligence. This is because log files, while incredibly valuable for information security and threat detection, can also be a system vulnerability. Therefore, they must be protected at all times and accessible to those who need them.
This article explores the challenges of implementing zero-trust log file intelligence and how emerging technologies can address these challenges.
Log files: they reveal everything
Log files are digital records that reveal information about a system’s activities. They are a crucial source of information because by analyzing them, organizations can gain valuable insights into network performance, identify vulnerabilities and detect suspicious activity.
However, their value is also their threat. As if they reveal everything, those who have access to them also know everything. For example, an attacker can use log files to track user activity, identify privileged accounts, and steal sensitive information. Once they use that information to gain access to the system, they can use log files to manipulate, steal, or hold critical information for ransom.
It is therefore critical to manage access to log files throughout the workflow to ensure the absolute minimum possible access for analysts and cybersecurity personnel and protect them from exposure.
Step one: secure collection and storage
To protect the integrity and security of log files, collecting and storing them in real time in a tamper-proof and isolated environment is critical. One way to manage the collection of this large-scale log file data is with OpenTelemetry. Its standardized approach and ability to integrate with various backends, including Postgres, make it a go-to option.
Blockchain technology, meanwhile, offers an ideal solution for its storage. Its immutable nature ensures that logs cannot be modified, preserving their integrity and ensuring compliant and transparent recording. Furthermore, the decentralized nature of blockchain reduces the risk of an attack without any focal point.
Step two: access control with minimal privileges
Secure log management requires a balance between security and productivity to ensure that logs are never made public, while still allowing them to be analyzed. This poses a challenge to traditional access controls such as data classification, masking, and query-based access because, while they can limit exposure, they can also hinder threat detection and analyst efficiency. They are also not completely secure and the decrypted log files are still widely accessed.
One way to achieve the least privileged access control without compromising productivity is homomorphic encryption, a cryptographic solution that ensures data remains encrypted throughout its lifecycle. This is because those who need access to threat intelligence logs can use homomorphic encryption to analyze them in an encrypted state without actually being able to read them.
This encrypted access control can also be extended beyond the analysts to anyone involved in log management. For example, administrators can manage permissions and access to the logs and audit access requests without ever being able to read the logs themselves, while they remain encrypted. This applies to the full breadth of zero-trust systems that use homomorphic encryption, where administrators and super-users do not have the ability to read the data under their care but can still manage it.
Step three: threat intelligence and response
It is critical to limit the amount of data shared outside the secure system to prevent potential exposure and the creation of vulnerable entry points. A possible solution to this is to use native AI for the analysis instead of third-party tools.
For example, a private Small Language Model (SLM) AI working in the database could provide specialized insights and machine learning on the encoded data without that data ever being shared externally from the system. In addition, because it is an SLM, the results have the potential to be more accurate and free from AI hallucinations as the model is not trained on large amounts of data that may be inaccurate or biased and instead only works on the encrypted log file data and all relevant sources given.
Because the logs remain encrypted at all times and access to analyze the encrypted logs is only granted on a least privilege basis, strict zero-trust security is maintained.
Final thoughts
This article has shown that zero-trust is feasible when it comes to the complex issue of log file intelligence and management, and is optimal for security and privacy. After all, logs should never be made public and should never be edited. What better way to achieve this than an immutable system of zero-trust access?
Even if you don’t take a zero-trust approach to your log management and intelligence, it’s still critical to keep this vital data pool protected at all times, even when it’s in use.
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