Cyber targets for 2025

Let us imagine that there is a room somewhere in Russia (but it could be anywhere else hostile to the West) and it’s full of hackers plotting their attacks for 2025. You can imagine that they are sharing stories of their successes in 2024. How they have targeted people with phishing emails and got them to open malware or download (unwittingly) malware that has not only given the hackers access to the servers of that company, but every other company in the supply chain.

The next hacker speaks up explaining how he has got round the security of cloud providers and managed to get into a variety of organizations that way. He proudly explains that he hasn’t even exploited some of those hacks yet. They are now easy targets for the New Year.

A third hacker explains how he managed to access a security update to a frequently used piece of software, and how he had added a back door that no-one had spotted. So, when everyone downloaded the software and patched the vulnerability, they introduced a back door that only he knew about. He suggested that this time next year he would be rich from all the ransoms he was going to collect.

Another hacker jumps up and explains that he was using AI to automate ransomware attacks, and he is making lots of dosh from the people who were paying him for the Ransomware as a Service software – sometimes people with very little IT knowledge – and were then using it to attack companies that had upset them in some way.

Lots of other people want to speak up with stories of how they had attacked companies and made money, but everyone stops speaking as an old general gets to his feet. He looks very stern but smiles as he starts to speak. “Comrades”, he says, “you have all done very well attacking companies in the West.” He pauses and his face takes on a sternness that had scared many a junior officer. He continues, “The problem is this: we have not defeated the West. What I need you to do is find some way to bring down the whole infrastructure of western society. Can you do that?”

The hackers look round at each other, until one speaks up. “Capitalist society depends on capital.” The audience is not overimpressed by the obviousness of the comment. There is much murmuring from the audience, but the hacker continues, “Why don’t we attack the banks and all the other financial institutions in North America and Europe. If they don’t have access to money, everything else will come to a stop.” The crowd nods in agreement. Some make additional useful comments to each other.

“How do we do that?” asks the general. “We attack the mainframes that are used by most of these organizations”, replies the hacker. And that’s what they do. Attacks by people who understood Windows and Linux continue in all their forms, but a large tranche of the technical people are given the job of understanding how mainframes work and their vulnerabilities. After all, the majority of financial institutions use mainframes. A subgroup is given the task of looking at employees on mainframes and seeing which ones could be manipulated into giving access to these fintech mainframes. They are looking for staff with drug habits and staff with financial problems or other issues that could be used against them. Another group has the task of getting keyloggers onto the laptops of systems programmers at mainframe sites.

A list of potential hacking techniques that have been used before are circulated amongst the hackers for them to see which still work and are useful for others to try.

They could attack sites using CICS. There are automated tools like CICSpwn available that could be used to identify potential misconfigurations, which could then be used by the hackers to bypass authentication. They could use the CICS customer front end and try a simple brute force attack to find a userid and password that would get them into the system.

They could use FTP. Two things need to happen first – keylogger software needs to capture the login credentials from a systems programmer, and a ‘connection getter’ needs to identify where to FTP to. Commands can be written to upload malicious binaries, and JES/FTP commands can be used to execute those binaries.

They could use TN3270 emulation software for their attack. Provided they have some potential userids, they could try password spraying, ie a few commonly-used passwords can be tried against every userid on the system.

NJE allows one trusted mainframe to send a job to another mainframe that it’s connected to. Hackers could use NJE to spoof a mainframe or submit a job and gain access to that other mainframe.

Then there’s potential vulnerabilities in Linux and other non-IBM software (like Ansible, Java, etc) that runs on mainframes.

Other techniques are available, but it’s not the function of this blog to make the job of nation state hackers easier. It is the job of this blog to ensure that every mainframe site is doing everything it can to ensure that it is secure against all forms of attack, and that it has software installed that can alert staff at the earliest opportunity that an attack has started, and the defence software needs to be able to suspend any suspect jobs as soon as possible.

Meanwhile, meetings like the one I’ve envisaged are probably going on, and mainframe-using companies in the West are going to be the targets in 2025. Don’t let yours be one of them.

Rock solid AI – Granite on a mainframe

Let’s start with what people are familiar with, ChatGPT. ChatGPT is a highly-trained and clever chatbot. The GPT part of its name stands for Generative Pre-trained Transformer. Generative means that it can generate text or other forms of output. Pre-trained means that it has been trained on a large dataset. And Transformer refers to a type of neural network architecture enabling it to understand the relationships and dependencies between words in a piece of text. IBM’s Granite 3.0 is very similar to ChatGPT, except that it is optimized for specific enterprise applications rather than general queries.

Just a side note, I was wondering about the choice of name for the product. In the UK, the traditional gift for a 90^th anniversary is granite. I just wondered whether there was some kind of link. In 1933 IBM bought Electromatic Typewriters, but I can’t see the link. Or maybe I’ve been doing too many brain-training quizzes!

Granite was originally developed by IBM and intended for use on Watsonx along with other models. In May this year, IBM released the source code of four variations of Granite Code Models under Apache 2, allowing completely free use, modification, and sharing of the software.

In the original press release in September 2023, IBM said: “Recognizing that a single model will not fit the unique needs of every business use case, the Granite models are being developed in different sizes. These IBM models – built on a decoder-only architecture – aim to help businesses scale AI. For instance, businesses can use them to apply retrieval augmented generation for searching enterprise knowledge bases to generate tailored responses to customer inquiries; use summarization to condense long-form content – like contracts or call transcripts – into short descriptions; and deploy insight extraction and classification to determine factors like customer sentiment.”

The two sizes mentioned in that press release are the 8B and 2B models.

In October this year, Version 3.0 was released, which is made up of a number of models. In fact the press release tells us that “IBM Granite 3.0 release comprises: 

• Dense, general purpose LLMs: Granite-3.0-8B-Instruct, Granite-3.0-8B-Base, Granite-3.0-2B-Instruct and Granite-3.0-2B-Base.
• LLM-based input-output guardrail models: Granite-Guardian-3.0-8B, Granite-Guardian-3.0-2B
• Mixture of experts (MoE) models for minimum latency: Granite-3.0-3B-A800M-Instruct, Granite-3.0-1B-A400M-Instruct
• Speculative decoder for increased inference speed and efficiency: Granite-3.0-8B-Instruct-Accelerator.

Let’s put a little more flesh on the bones of those models:

• The base and instruction-tuned language models are designed for agentic workflows, Retrieval Augmented Generation (RAG), text summarization, text analytics and extraction, classification, and content generation.
• The decoder-only models are designed for code generative tasks, including code generation, code explanation, and code editing, and are trained with code written in 116 programming languages.
• The time series models are lightweight and pre-trained for time-series forecasting, and are optimized to run efficiently across a range of hardware configurations.
• Granite Guardian can safeguard AI by ensuring enterprise data security and mitigating risks across a variety of user prompts and LLM responses.
• Granite for geospatial data is an AI Foundation Model for Earth Observations created by NASA and IBM. It uses large-scale satellite and remote sensing data.

In case you didn’t know, agentic workflows refer to autonomous AI agents dynamically interacting with large language models (LLMs) to complete complex tasks and produce outputs that are orchestrated as part of a larger end-to-end business process automation.

Users can deploy open-source Granite models in production with Red Hat Enterprise Linux AI and watsonx, at scale. Users can build faster with capabilities such as tool-calling, 12 languages, multi-modal adaptors (coming soon), and more, IBM tells us.

IBM is claiming that Granite 3.0 is cheaper to use compared to previous versions and other LLM (large language models) such as GPT-4 and Llama

IBM also tested the Granite Guardian against other guardrail models in terms of their ability to detect and avoid harmful information, violence, explicit content, substance abuse, and personal identifying information, showing it made AI applications safer and more trusted.

We’re told that the Granite code models range from 3 billion to 34 billion parameters and have been trained on 116 programming languages and 3 to 4 terabytes of tokens, combining extensive code data and natural language datasets. If you want to get your hands on them, the models are available from Hugging Face, GitHub, Watsonx.ai, and Red Hat Enterprise Linux (RHEL) AI. A curated set of the Granite 3.0 models can be found on Ollama and Replicate.

At the same time, IBM released a new version of watsonx Code Assistant for application development. The product leverages Granite models to augment developer skill sets, simplifying and automating their development and modernization efforts. It simplifies and accelerates coding workflows across Python, Java, C, C++, Go, JavaScript, Typescript and more.

Users can download the IBM Granite.Code (which is part of the watsonx Code Assistant product portfolio) extension for Visual Studio Code to unlock the full potential of the Granite code model from here.

It seems to me that the Granite product line is a great way for organizations to make use of AI both on and off the mainframe. I’m looking forward to seeing what they announce with Granite 4.0 and other future versions.

 

Tell me about ONNX and mainframe AI

Let’s start by finding out what ONNX is. It stands for Open Neural Network eXchange, and it’s described as an open-source AI (artificial intelligence) ecosystem with the aim of establishing open standards for representing machine learning algorithms and software tools to promote innovation and collaboration. You can get it from GitHub.

To put that another way, it means you can create and train AI models on any platform that you like, using any framework (eg PyTorch, TensorFlow, Caffe2, Scikit-learn, etc) you like, and ‘translate’ that into a standard format that can then be run on any other platform – and the one that we’re interested in is the mainframe.

ONNX was originally called Toffee and was developed by a team from Facebook, but was renamed in 2017. It’s supported by IBM, Microsoft, Huawei, Intel, AMD, Arm, Qualcomm, and others.

Developers may want to use different frameworks for a project because particular frameworks may be better suited to specific phases of the development process, such as fast training, network architecture flexibility, or inferencing on mobile devices. ONNX then facilitates the seamless exchange and sharing of models across many different deep learning frameworks. Another advantage of using ONNX is that it allows hardware vendors and others to improve the performance of artificial neural networks of multiple frameworks at once by targeting the ONNX representation.

ONNX provides definitions of an extensible computation graph model, built-in operators and standard data types, focused on inferencing (evaluation). Each computation dataflow graph is a list of nodes that form an acyclic graph. Nodes have inputs and outputs. Each node is a call to an operator. Metadata documents the graph. Built-in operators are to be available on each ONNX-supporting framework. Thanks to Wikipedia for the information in this format.

So, we saw in that list of vendors that IBM is involved in the project. How is ONNX used on a mainframe? I know part of the answer to that because I watched a fascinating presentation by Megan E Hampton, IBM – Advisory Software Engineer, at the excellent GSE UK conference at the start of the month. Here’s what she told her audience.

Currently, on the mainframe, there aren’t many tools available for the optimization of AI models. That’s where ONNX comes in. It is an open format for representing AI models. ONNX defines a computation graph model, as well as definitions of built-in operators and standard data types.

ONNX uses a standard format for representing machine learning (ML) and deep learning (DL) models. ONNX models are generated by supported DL and ML frameworks or converted from other formats by converting tools. ONNX models can be imported into multiple frameworks and runtime engines and executed/accelerated by heterogeneous hardware and execution environments.

Among the benefits of using ONNX on a mainframe are that it:

• Allows clients to use popular tools and frameworks to build and train.
• Makes assets portable to multiple Z operating systems.
• Optimizes and enables seamless use of IBM Z hardware and software acceleration investments.

But what’s the next stage? How do you get from an AI model to something useful that can run on a mainframe? That’s where the IBM Z Deep Learning Compiler (zDLC) come in. It uses open source ONNX-MLIR to compile .onnx deep learning AI models into shared libraries. The resulting shared libraries can then be integrated into C, C++, Java, or Python applications.

zDLC takes the ONNX (model) as input, and generates a single binary. It handles static and dynamic shapes as well as multiple data representations. And it exploits parallelism via OpenMP. OpenMP (Open Multi-Processing) is an application programming interface (API) that supports multi-platform shared-memory multiprocessing programming in C, C++, and Fortran. It consists of a set of compiler directives, library routines, and environment variables that influence run-time behaviour.

Multi-level intermediate representation (MLIR) significantly reduces the cost of building domain specific compilers. It connects existing compilers together through a shared infrastructure. It’s part of LLVM compiler and follows LLVM governance. LLVM and MLIR are new and powerful ways of writing compilers that are modular and generic. MLIR is flexible, and introduced the concept of ‘dialects’.

Think of it like this:

ONNX (the AI model) plus MLIR (the compiler) produces ONNX-MLIR | IBM Z Deep Learning Compiler (ie it compiles the AI models).

So, just to explain these further, MLIR is a unifying software framework for compiler development. It is a sub-project of the LLVM Compiler Infrastructure project.

LLVM is a set of compiler and toolchain technologies that can be used to develop a frontend for any programming language and a backend for any instruction set architecture. LLVM is designed around a language-independent intermediate representation (IR) that serves as a portable, high-level assembly language that can be optimized with a variety of transformations over multiple passes. Interestingly, LLVM isn't an acronym, although, originally, it stood for Low Level Virtual Machine.

Let’s go back to the mainframe again, we can build and train a model in any popular framework (PyTorch, TensorFlow, etc) on any platform, which allows the maximum flexibility possible. Then on the mainframe, we can then use ONNX. Models are converted to the ONNX interchange format. We can then leverage z/OS Container Extensions (zCX) if we want to run the application inside a Docker container on z/OS as part of a z/OS workload. We can also run the applications on zIIP engines, which won’t impact the 4-hour rolling average cost of general processors. The IBM zDLC (Deep Learning Compiler) enables existing models to quickly and easily take advantage of the IBM z16 Telum processor's Integrated Accelerator for AI.

Looking at the Deep Learning Compiler Flow: the ONNX model (dialect) is lowered and transformed through multiple phases of intermediate representation (IR) to a dialect that can be processed by an LLVM compiler. The output of the LLVM compilation and build is a shared library object that can be deployed.

It all seems so simple when it’s explained. I expect we’re going to hear a lot more about all this.

 

More on security

Following on from last week’s blog entitled Insider threats and smf, I recently got a press release from application security SaaS company Indusface giving some figures to the problem that organizations are facing from their own employees. It’s not just that there are a very small minority of employees who seem intent on bringing their company down by deleting data or launching ransomware attacks, there also seems to be a huge pool of people who inadvertently give away information, or open malware, or click on ‘dodgy’ links that leave companies wide open to serious attacks by bad actors.

The people at Indusface have used global search data from AHrefs to find the world's top five questions and concerns about cyber security in the workplace. The data from AHrefs, which was correct as of October 2024, can be found here. They have then come up with their own suggested answers to those searches.

I’d like to start with the question that came in fourth place, which was “What percentage of breaches are human error responsible for?” There were similar searches on “Human error cyber security”

Their answer was: “According to data by Indusface, 98% of all cyber-attacks rely on human error or a form of social engineering. Special engineering breaches leverage human error, emotions, and mistakes rather than exploiting technical vulnerabilities. Hackers often use psychological manipulation, which may involve coaxing employees to reveal sensitive information, download malicious software, or unknowingly clicking on harmful links. Unlike traditional cyberattacks that rely on brute force, social engineering requires direct interaction between attacker and victim.

“Given that human error can be a major weak link in cyber security, the best way to prevent these attacks is to put in place education and training on the types of attacks to expect and how to avoid these. That said, implementing a zero-trust architecture, where requests for every resource are vetted against an access policy, will be paramount in stopping attacks from spreading even when a human error results in a breach. Also, make sure that the applications are pen tested for business logic and privilege escalation vulnerabilities so that the damage is minimized.

“Basics such as standard best practices across the board, secure communications, knowing which emails to open, when to raise red flags, and exercising extreme caution when accepting offers will go a long way in preventing human errors that lead to breaches.”

Let’s look at the other search terms in the top five. In first place, with the most searches, was. “Why is cyber security training so important for business?” There were similar searches for “Cyber security for business”.

The answer from Indusface was: “With data breaches costing businesses an average of $4.45 million globally in the last year (according to IBM’s Cost of a Data Breach Report 2024), it raises the question of just how critical it is for organizations to provide employees with comprehensive training on what constitutes sensitive data and how they can protect it, as well as what is at stake if they do not adhere to the policies.

“And training doesn’t have to be monotonous, for example set up phishing email simulators to engage the team and allow them to see the potential dangers in action. These simulations show how quickly and easily attacks can happen, helping employees develop practical, hands-on skills for spotting suspicious activity.

“Cybersecurity threats evolve constantly, so training should be regular, not a one-time event. Regular training and guidance will ensure that employees receive tailored guidance on securing their work equipment, home offices, use of VPNs, and recognizing the unique threats posed by both in-office and home working environments.”

The second most frequent searches were “How is AI used in cyber security?” or simply “Cyber Security AI”.

Indusface said: “The biggest problem with security software, especially website and API protection is the prevalence of false positives. False positives are when legitimate users are prevented from accessing an application. So notorious is this problem that 50%+ of businesses worldwide have implemented Web Application and API Protection/ Web Application Firewall (WAAP/WAF) solutions and left them on log mode. This means that attacks go through the WAF and they are at best used as log analysis tools after a breach.

“Effectively using AI can help with eliminating or reducing false positives to a bare minimum and encourage more businesses to deploy WAFs in block mode.

“The other problem with security software is letting an attack go through. These are also called false negatives. Using AI on past user behaviour and attack logs can effectively prevent any attacks that don’t conform to typical user behaviour.”

Third in their list was “How can you protect your home computer?” and “Home cyber security”. They suggest that by 2025, according to a Forbes’ article, approximately 22% of workers will work remotely. They go on to ask, with such a significant increase in remote roles, how can employers ensure their employees' home computer remains protected?

Their answer was: “Remote working means people are working in less secure environments and their devices are more exposed to data breaches both digitally and physically. Many remote workers are using the same device for professional and personal use, or even accessing company data on devices shared with other household members.

“Employers should ensure strong password management, including using automatic password generators that create extra secure passwords, and never duplicate these across accounts. Multi-factor authentication also provides a secure method of verifying your identity, making it harder for hackers to breach any accounts. Limiting what could be accessed on official devices is also important in thwarting attacks.

“That said, installing endpoint security software like antivirus, and keeping it updated, should be enough to protect most computers, unless you fall victim to an advanced phishing attack.”

The fifth most popular searches were, “What are the top 3 targeted industries for cyber-attacks?” and “Top industries cyber-attack”.

Here’s what Indusface said: “According to EC University, manufacturing, professional / business, and healthcare are the top 3 targeted industries.

“The manufacturing sector leads the world in cybercrime incidents according to Statista (2023). Attacks on the industry range from halting production lines, to the theft of intellectual property, and compromising the integrity of supply chains.

“The professional, business, and consumer services sector has also become an attractive target for cybercriminals due to its heavy reliance on sensitive data. Confidential client information and business insights are often targeted, leading to significant financial losses and damage to brand reputation, and client relationships.

“A breach in the healthcare industry can have dire consequences, from compromising sensitive patient data to disrupting critical medical services. Given the high value of medical records on the black market, there is an urgent need for stronger cybersecurity measures to protect both patient privacy and the integrity of healthcare systems.”

I thought it was useful to get another view on the ongoing issue of keeping your mainframe – and any other platforms your organization supports – safe from breaches. And keeping your employees alert at all times to potential threats.

Insider threats and SMF

Many people think that SMF records will tell you everything that has happened at a site. And, if you link it to some kind of alerting software, it will act as the cornerstone of your mainframe’s security. And that, as they sleep snuggly in their beds at night, is their mainframe security done and dusted.

Many people think that all the people who work for their organization and access their mainframes are intelligent and trustworthy, and are not really worth worrying about when their main focus should be on gangs trying to extort money or hostile nation states trying destroy their country’s competitors, or just damage the infrastructure of any country they view as hostile to them. That’s where an organization’s main security focus should be, surely?

Let’s start by deciding what an insider threat actually is. Let’s start with people who are employed by an organization. They have a valid userid and password and have a legitimate right to be accessing the mainframe. Now, every so often, humans will make mistakes. Some are small – and some can be quite major. It may be the case that your trusted insider accidentally deletes files or makes some other changes to the mainframe. Provided that person owns up straightaway, the IT team can usually solve the problem fairly promptly. Files can be restored from backups before other batch jobs that use those files are scheduled to run. And chaos can be averted.

Other insiders may be more malicious. They may have not got the internal promotion they were expecting or the pay rise that they needed. Other members of staff may have problems outside of the office, for example an increasing drug habit or an increasing use of alcohol. They may be running up gambling debts as they try to win back the money they have lost. Both groups are a problem. The disgruntled insiders may well deliberately cause damage to data or applications. They may have the authority to make other changes. And the second group of addicted users may well be manipulated by organized crime to infect the mainframe with some kind of malware that the bad actors associated with those criminals can use to launch a ransomware attack.

These days, the disgruntled employs can access Ransomware as a Service (RaaS) applications and launch an attack on the mainframe – hoping that the money they get from the ransom will compensate them for the money the company didn’t give them. It will also have to be enough to support their lifestyle once they go on the run.

Criminal gangs are also on the look out for credentials that can get them into the mainframe. Disgruntled staff or employees who need money to fund their habits will be approached and offered money for their userids and passwords. Using these, the bad actors can do what they want on the mainframe, safe in the knowledge that most tools processing SMF records won’t identify unusual activity by those accounts.

There’s another group of employees that might be targeted by criminal gangs, and those are people who need money. It may be that an ageing relative needs to go into a home and they need money to pay for that relative’s care. It may be that a family member needs an operation that needs to be paid for. Or a family member may need an expensive medication that they will have to pay for. These people may be vulnerable to exploitation by criminal gangs.

Of course, ordinary members of staff may be tricked by the use of an AI simulating the voice of their manager, who asks to ‘borrow’ the employee’s userid and password to do some work over the weekend.

Typically, security tools won’t send alerts if valid userids and passwords are used. And if the settings are changed so that an alert is sent, you get the situation where staff get so many false positives that they tend to ignore the messages.

Let’s see what the Cost of a Data Breach Report 2024 from IBM had to say about insider threats. The report says that the global average cost of a data breach in 2024 is US$4.88m, and the USA has the highest average data breach cost at US$9.36m. Compared to other vectors, malicious insider attacks resulted in the highest costs, averaging US$4.99 million. It goes on to say that among other expensive attack vectors were business email compromise, phishing, social engineering, and stolen or compromised credentials.

Using compromised credentials benefited attackers in 16% of breaches. Compromised credential attacks can also be costly for organizations, accounting for an average US$4.81 million per breach. Phishing came in a close second, at 15% of attack vectors, but in the end cost more, at US$4.88 million. Malicious insider attacks were only 7% of all breach pathways.

The report also found that the average time to identify and contain a breach fell to 258 days, however, whether credentials were stolen or used by malicious insiders, attack identification and containment time increased to an average combined time of 292 and 287 days respectively.

So, while insider threats aren’t the biggest threat to your mainframe, they are still a significant threat in the amount of money they can cost your organization as well as the amount of time it will take to recover from the attack. SMF is great, but security tools don’t usually send alerts when there is unusual activity by the accounts used by employees. So, these activities aren’t identified straight away and won’t be halted. Obviously, file integrity monitoring software would solve that problem before it became a serious problem. It would be able to identify an unusual activity and immediately suspend the job or user, and then send an alert. If it were a real systems programmer working at 2 in the morning from, say, Outer Mongolia, then, once this is confirmed, the job can be allowed to continue. But if you don’t have that type of software installed, guess what’s going to be filling your time for the next 258 days!

What I’m suggesting is that insider threats are a real issue, and SMF on its own isn’t enough.

Is anyone really using AI on a mainframe?

We read a lot about artificial intelligence (AI) these days, and random people on LinkedIn message me about specific AI applications (not mainframe-based), but how can we really know what other sites are actually doing with AI on their mainframes?

Firstly, there was the Kyndryl survey that I wrote about in September. You can read it here. And now we have got the results from BMC’s mainframe survey, which you can find here. Their survey found that 45% of respondents listed artificial intelligence for IT operators (AIOps) and operational analytics as a top priority. The survey also found that 31% of respondents who have implemented AIOps perceive complexity as a major issue Tin addition, the survey found that 60% of extra-large mainframe organizations which are prioritizing AIOps are looking to solve this AIOps complexity issue using GenAI solutions, while 57 percent are using machine learning (ML)-based automation.

So, how many sites have actually got their hands dirty and are using some kind of AI? The survey found that 76% of organizations are using Generative AI (GenAI). GenAI is a type of AI that can create new content like images, videos, text, code, music, and audio. Analysing the data in a slightly different way, the survey found that 86% of respondents who are increasing their mainframe investment are using GenAI. It goes on to suggest that organizations with a flat or decreasing investment in their mainframe systems are significantly less likely to be using GenAI. The survey also found that 82% of those sites increasing their mainframe investment have a GenAI policy in place. I think the need for a GenAI policy cannot be overemphasized, and I pleased to see so many sites have one in place.

What benefits are those sites using GenAI finding they’re getting? The survey found that the benefits included significant improvements in efficiency and operational performance, with 40% reporting notable advancements. Where organizations were prioritizing AIOps, 45% of sites reported that GenAI is the most important capability to help them achieve their objectives.

What are the benefits of using GenAI to automate and optimize IT operations? The survey highlighted four areas, which were: 

• Automation: 37% of organizations want to use GenAI to eliminate repetitive tasks, improving efficiency and freeing up resources for strategic activities. 
• Identifying issues and risks: 36% of organizations want to analyse code and configuration files to identify problems and vulnerabilities, enhancing security. 
• Gaining insights: 34% of organizations want to augment existing expertise with critical business insights, supporting decision-making processes. 
• Training: 33% of organizations plan to use GenAI for onboarding and training new personnel, effectively bridging the knowledge gap.

What can we learn from this? I think we’re well past the toe-in-the-water stage of AI use on a mainframe. However, I’d like to see those figures cross the 50% threshold in order to view AI as completely accepted as a mainframe technology. From my own personal interest in mainframe security, I’d like to see close to 100% of sites using AI as part of their security posture against malware, ransomware, and people using AI as an easy way of breaching an organizations mainframe security.

Let’s take a quick look at some of the other results from that survey. 94% of respondents viewed the mainframe as a long-term platform or a platform for new workloads, which is heartening. And 90% of respondents said that their organizations are continuing to invest in their mainframes – hooray!

What priorities did they find in the survey? 64% of respondents had compliance and security as top of their list. Ransomware is also high on people’s agenda, but, worryingly, there was an 8% drop in those sites that found their ransomware controls to be extremely effective. As I’ve written about before, the bad actors are making it easier for non-experts to use their technology to breach mainframes. Cost optimization was also a top priority, and so was AIOps. Other respondents are looking at connecting mainframes to cloud-based workloads, and utilizing a cloud-based mainframe (mainframe as a service).

The survey also found that the use of Java for mainframe code is increasing. This, they suggest is not only because organizations want code that is accessible across platforms, but also because it allows developers to write mainframe code without needing additional training. The survey found both an increase in new applications being written in Java, as well as existing applications being rewritten in Java.

I always find surveys interesting to see what is going on at mainframe sites – or at least at the mainframe sites that are prepared to complete surveys. I think, the most significant result is the growth in the use of artificial intelligence on mainframes. So, to answer my title question, yes, people are using AI on the mainframe.

If you do like completing mainframe surveys, look out for the Arcati Mainframe Yearbook’s survey later in the year. You can find the whole thing, including the 2024 user survey report here.

Zowe LTS V3 released

Zowe the open source-software from the Open Mainframe Project of the Linux Foundation was originally launched to make it easy for IT specialists with no mainframe experience to be able to access and utilize data and applications on z/OS, using their knowledge and experience of tools that previously weren’t available on mainframes.

The Open Mainframe Project (OMP) describes Zowe as an open-source software framework for the mainframe that strengthens integration with modern enterprise processes and tools, offers vendors and customers the ability to execute on modernization initiatives with stability, security, interoperability, as well as easy installation and a continuous delivery model for receiving upgraded features.

On 3 October, the OMP announced the launch of Zowe’s Long Term Support (LTS) V3 Release. 

For mainframers who are still a little unfamiliar Zowe, the press release tells us that it’s an integrated and extensible open-source framework for z/OS, and that it comes with a core set of applications out of the box in combination with the APIs and OS capabilities future applications will depend on. It offers modern interfaces to interact with z/OS and allows users to work with z/OS in a way that is similar to how they will have worked on cloud platforms. Developers can use these interfaces as delivered or through plug-ins and extensions that are created by clients or third-party vendors. For example, Zowe V3 offers new support for the IntelliJ Zowe Explorer plugin as well as the simplified install wizard.

The press release lists some of the benefits of the LTS V3 including:

• Durability: a refreshed number of core components that make up the software stack to give a secure stable shelf life, which ensures years of use with continued updates and support.
• Stability: the installation and configuration have been stabilized through V3. Organizations can confidently adopt the technology for enterprise use and upgrade when appropriate for their environment, minimizing the risk of disruption.
• Enhanced security: an enhanced security posture by actively monitoring dependencies and upgrading them proactively. This helps mitigate risks associated with outdated or vulnerable dependencies, offering more robust security features compared to earlier versions.

The new release of Zowe increases product durability, stability, and security with the support of a large open-source community and a Conformance Program.

Because of my long association with the Arcati Mainframe Yearbook, I am always pleased to see its survey results quoted in press releases. This one says: “According to the Arcati Mainframe Yearbook 2024, the independent annual guide for users of mainframe systems, 85% of mainframe organizations will be adopting Zowe by the end of the year or have already adopted it into their modern enterprise solutions.”

“The continued success of Zowe as a community-driven project highlights the importance of the mainframe as an open platform supporting hybrid cloud architectures”, said George Decandio, chief technology officer, Mainframe Software Division, Broadcom. “The latest V3 release introduces new components that expand capabilities to client SDKs and additional IDEs, reflecting Zowe’s ongoing evolution to meet the needs of the mainframe ecosystem. Notably, this update enhances the Zowe API Mediation Layer, a key component our customers view as essential in transforming the role of the mainframe in their multi-platform environments.”

“Zowe’s progress underscores a broader commitment to open, interoperable standards, enabling organizations to maximize the value of their mainframe and IT infrastructure investments”, said Decandio. “Broadcom is proud to be a leading contributor to this community and is committed to supporting the project’s continued growth.”

“Zowe V3 is the culmination of five years of work by volunteers from around the world”, said Bruce Armstrong, IBM Z Principal Product Manager at IBM and member of the Zowe Advisory Council (ZAC). “I am particularly proud of the fact that Zowe has revolutionized access to z/OS-based services for thousands of next-generation developers and system programmers that will continue the platform’s success for decades to come.”

“Rocket Software is a proud founding contributor of Zowe”, said Tim Willging, Fellow and VP of Software Engineering at Rocket Software. “It’s been incredible to see the success and passion of the open-source community in supporting hybrid cloud initiatives. The expanded capabilities in the V3 release will help accelerate an organization’s modernization journey and provide them with enhanced security, maintainability, and scalability needed to match their customers’ needs – now and in the future.”

Zowe is a contributor-led community with participating vendors such as, but are not limited to, Broadcom, IBM, Phoenix Software, Rocket Software, and Vicom Infinity. As a result of their extensive collaboration, the following Zowe extensions have been transformed in Zowe V3:

• Explorer for Intellij provides the developers within the IntelliJ IDEs with the capability to work with the z/OS platform.
• Kotlin and Java SDKs are Generally Available Extensions simplifying interaction with z/OS from the Java and Kotlin applications.
• The IMS service and the current CLI extensions are archived. IBM is working on replacements.
• The Zowe Conformance Program is updated with LTS V3 Guidelines.

Aimed to build a vendor-neutral ecosystem around Zowe, the OMP’s Zowe Conformance Program was launched in 2019. The program has helped OMP members incorporate Zowe with new and existing products that enable integration of mainframe applications and data across the enterprise.

To date, 77 products have implemented extensions based on the Zowe framework and earned these members conformance badges.

Additional resources include the Zowe GitHub Repository, the Zowe Community Website, and the Getting Started documentation site.

The Open Mainframe Project is an open source initiative that enables collaboration across the mainframe community to develop shared tool sets and resources. It is intended to serve as a focal point for deployment and use of Linux and open source in a mainframe computing environment. With a vision of open source on the mainframe as the standard for enterprise-class systems and applications, the project’s mission is to build community and adoption of open source on the mainframe by eliminating barriers to open source adoption on the mainframe, demonstrating value of the mainframe on technical and business levels, and strengthening collaboration points and resources for the community to thrive.