TISAX, the “Trusted Information Security Assessment Exchange”, is a mechanism for checking and exchanging test results according to industry-specific standards. Originally created as a system for the exchange of standardized test results in the automotive industry, it is optimized for the risk assessment of suppliers. Therefore, TISAX is being developed and governed by the ENX Association and published by the German Association of the Automotive Industry (VDA). Its focus lies on secure information processing between business partners, protection of prototypes and data protection in accordance with the EU’s General Data Protection Regulation (GDPR) for potential deals between car manufacturers and their service providers or suppliers.

As a crucial part of a secure supply chain, TISAX is a standard for Information Security Management Systems (ISMS), originally derived from the ISO/IEC 27001 standard in 2017, but has since diverged. For the automotive industry, TISAX brings standardization, quality assurance and guarantees information security measures are assessed by audit providers in accordance with the VDA standards. Audits according to TISAX, especially for service providers and suppliers, are carried out by so-called “TISAX audit service providers” and come with three levels of maturity an overview of which you can find in the TISAX Participant Handbook and on websites of certification providers like Adacor (German only).

Greenbone’s certifications increase our products’ value for our customers, not just by saving time and money, but also by proving our outstanding security level and high standards. Elmar Geese, CIO at Greenbone: “With TISAX, we document our independently audited security status. Customers do not need to do individual assessments, work with lengthy questionnaires or all the other things needed in a bottom-up audit. We guarantee that we meet their security requirements.”

Therefore, Greenbone follows the question catalogue of information security of the German Association of the Automotive Industry (VDA ISA). The assessment was conducted by an audit provider. The result is exclusively retrievable via the ENX portal (Scope ID: S3LW9L, Assessment ID: A1P7V9).

What seems somewhat strange in the case of physical infrastructure – who would recreate a break-in to test their alarm system – is a tried and tested method in IT for identifying vulnerabilities. This so-called active scanning can be performed daily and automatically. Passive scanning, on the other hand, detects an intrusion in progress, because every cyber intrusion also leaves traces, albeit often hidden.

Controlling the Traffic

Firewalls and antivirus programs, for example, use passive scanning to check traffic reaching a system. This data is then checked against a database. Information about malware, unsafe requests and other anomalies is stored there. For example, if the firewall receives a request from an insecure sender that wants to read out users’ profile data, it rejects the request. The system itself is unaware of this because the passive scan does not access the system but only the data traffic.

The advantage of this is the fact that the system does not have to use any additional computing power. Despite the scan, the full bandwidth can be used. This is particularly useful for critical components. They should have the highest possible availability. The fewer additional activities they perform, the better.

The disadvantage of passive scanning is that only systems that are actively communicating by themselves can be seen. This does not include office software or PDF readers, for example. But even services that do communicate do so primarily with their main functions. Functions with vulnerabilities that are rarely or not at all used in direct operation are not visible, or are only visible when the attack is already in progress.

Checking the Infrastructure

Active scans work differently and simulate attacks. They make requests to the system and thereby try to trigger different reactions. For example, the active scanner sends a request for data transfer to various programs in the system. If one of the programs responds and forwards the data to the simulated unauthorized location, the scanner has found a security hole.

Left: Active scans send queries to the system in an attempt to trigger different responses. Right: Passive scans check the traffic reaching a system and match this data against a database.

The advantage: the data quality that can be achieved with active scanning is higher than with passive scanning. Since interaction takes place directly with software and interfaces, problems can be identified in programs that do not normally communicate directly with the network. This is also how vulnerabilities are discovered in programs such as Office applications.

However, when interacting directly, systems have to handle extra requests which may then affect the basic functions of a program. Operating technology such as machine control systems, for example, are not necessarily designed to perform secondary tasks. Here, scanning under supervision and, as a supplement, continuous passive scanning are recommended.

Scanning Actively, but Minimally Invasive

Nevertheless, active scanning is essential for operational cyber security. This is because the risk posed by the short-term overuse of a system component is small compared to a production outage or data leak. Moreover, active scans not only uncover vulnerabilities, they can also enhance passive scans. For example, the vulnerabilities that are detected can be added to firewall databases. This also helps other companies that use similar systems.

Active and Passive Scanning Work Hand in Hand

Since the passive scanner can also provide the active scanner with helpful information, such as information about cell phones or properties about network services, these two security tools can be considered as complementary. What they both have in common is that they always automatically get the best out of the given situation in the network. For the passive and active scanning techniques, it does not matter which or how many components and programs the network consists of. Both security technologies recognize this by themselves and adjust to it. Only with a higher level of security does the optimized tuning of network and scanners begin.

So it is not a question of whether to use one or the other. Both methods are necessary to ensure a secure network environment. A purely passive approach will not help in many cases. Proactive vulnerability management requires active scans and tools to manage them. This is what Greenbone’s vulnerability management products provide.

In the course of the war in Ukraine, a closed-source provider like Kaspersky is hit at its weakest point. Because its customers must believe something that they want to know, and in critical areas of use even have to know: that the use of a software does not involve any risks that cannot be audited.

The vendor tried to meet this requirement without making its sources open source, through so-called transparency centers where source code may be viewed. For various reasons, this is no longer enough for customers.

The current cause is the war in Ukraine and ultimately the fact that it is a Russian company, but the reasons and causes lie deeper. Ultimately, not only Russian providers are affected by the fundamental problem. Software (and hardware), just like the data it processes, can only be trusted if the sources are open and the production process is transparent.

We already know the problem from other contexts – whether a construct is called “Transparency Center”, “Safe Harbour” or “Privacy Shield” – in the end these are marketing terms that cannot disguise the fact that they cannot provide the transparency and trust that we need for secure digital infrastructures. Only open source can do that.

Project lead Jennifer Außendorf

Identifying tomorrow’s vulnerabilities today with Predictive Vulnerability Management: Together with international partners from across Europe, Greenbone’s cyber security experts are developing a novel cyber resilience platform that uses artificial intelligence and machine learning to detect vulnerabilities before they can be exploited, helping to prevent attacks.

Greenbone is strengthening its internal research in the field of “Predictive Vulnerability Management” and will additionally participate in publicly funded research and development projects in 2022. Currently, the security experts are working on a funding application for a European Union project. Until the first phase of the application submission is completed, Greenbone is involved within an international consortium and is working on a joint cyber resilience platform. The focus here is on preventing attacks in advance so that remedial action can be taken more quickly in an acute emergency. Methods for detecting anomalies by combining and analyzing a wide variety of sources from network monitoring and network analysis data will help to achieve this. The research area focuses on active defense against cyber attacks and includes penetration tests and their automation and improvement through machine learning.

In an interview, project manager Jennifer Außendorf explains what the term “Predictive Vulnerability Management” means.

Jennifer, what is cyber resilience all about? Predictive Vulnerability Management sounds so much like Minority Report, where the police unit “Precrime” hunted down criminals who would only commit crimes in the future.

Jennifer Außendorf: Predicting attacks is the only overlap, I think. The linchpin here is our Greenbone Cloud Service. It allows us to access very large amounts of data. We analyze it to enable prediction and remediation, providing both warnings for imminent threats and effective measures to address the vulnerabilities.

For example, we can also identify future threats earlier because we are constantly improving Predictive Vulnerability Management with machine learning. In the area of “Remediation”, we create a “reasoned action” capability for users: they are often overwhelmed by the number of vulnerabilities and find it difficult to assess which threats are acute and urgent based purely on CVSS scores.

One solution would be to provide a short list of the most critical current vulnerabilities – based on the results of artificial intelligence. This should consider even more influencing variables than the CVSS value, which tends to assess the technical severity. Such a solution should be user-friendly and accessible on a platform – of course strictly anonymized and GDPR-compliant.

Why is Greenbone going public with this now?

Jennifer Außendorf: On the one hand, this is an incredibly exciting topic for research, for which we provide the appropriate real-life data. The large amounts of data generated by the scans can be used in a variety of ways to protect customers. Figuring out what is possible with the data and how we can use that to add value for users and customers is a big challenge.

On the other hand, Greenbone wants to use the project to strengthen cyber security in the EU. For one thing, this is a hot topic right now: customers often end up with American companies when looking for cyber defenses, which usually doesn’t sit well with the GDPR. Greenbone has decided to launch a project consortium and will seek project funding in parallel.

Who will or should participate in the consortium?

Jennifer Außendorf: The consortium will consist of a handful of companies as the core of the group and will be complemented by research partners, technical partners for development and a user group of other partners and testers.

Because the project will take place at EU level, it is important for us to involve as many different member states as possible. We hope that the different backgrounds of the partners will generate creative ideas and approaches to solutions, from which the project can only benefit. This applies equally to the phase of building up the consortium.

Are there other players in the field of Predictive Vulnerability Management so far or has no one tried this yet?

Jennifer Außendorf: At the moment, we don’t see any competitors – Greenbone also deliberately wants to be an innovation driver here. Yes, the buzzwords “thought leadership”, “cloud repurpose” and “cyber resilience” are certainly floating around, but there is one thing that only we (and our customers) have: the anonymized data, which is essential for the research results, and above all the large amount of data that makes it possible to apply machine learning and other methods in connection with artificial intelligence in the first place – only we have that.

What is the current status there, what is on the roadmap?

Jennifer Außendorf: We are currently in the process of specifying the individual topics in more detail with the first research partners. They have many years of experience in cyber security and machine learning and provide very valuable input. We are also currently working on expanding the consortium and recruiting additional partners. Work on the actual application should start soon.

Our goal is to incorporate the results of the project directly into our products and thus make them available to our customers and users. Ultimately, they should benefit from the results and thus increase cyber resilience in their companies. That is the ultimate goal.