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.

 

AI is all the rage!

Perhaps no surprises there. Pixel phones now come with Gemini. My video editing software has AI integration. My Opera browser comes with AI. Just about everything has some sort of AI integrated. So, it won’t come as a shock to find mainframers are as keen on AI as everyone else.

That’s what Kyndryl are telling us based on the results of their recent State of Mainframe Modernization Survey. They say that 2024 is the year of AI adoption on the mainframe. The survey also found that modernization projects are delivering significant financial benefits, however, many organizations face skills shortages, preventing the transformation of complex mission-critical systems. Although, to be honest, that may be a good thing. After all, plenty of applications are best placed on a mainframe rather than trying to convert everything to run in the cloud. We’ve rehearsed the arguments for and against cloud in these blogs before, highlighting what works best in the cloud and what doesn’t.

Kyndryl surveyed 500 business and IT leaders and found that 86% of respondents are adopting AI and generative AI to accelerate their mainframe modernization initiatives. In addition, a third of respondents said that mainframes have become a foundation for running AI-enabled workloads. Lastly, almost half the people surveyed aim to use generative AI to unlock and transform critical mainframe data into actionable insights.

The survey also found that IT modernization projects and patterns are resulting in substantial business results, including triple-digit one-year return on investment (ROI) of 114% to 225%, and collective savings of $11.9 billion annually. Not surprisingly, most organizations have chosen a hybrid IT strategy.

According to the survey, 86% of respondents think that mainframes remain essential l (why not 100%?). In addition, the survey found that 96% of respondents are migrating a portion (on average that portion is 36%) of their applications to the cloud.

The survey found that organizations are running 56% of their critical workloads on a mainframe. Over half of the respondents said mainframe usage increased this year and 49% expect that trend to continue.

Other findings in the survey included the fact that many respondents are still facing a skills shortage, especially in new areas such as generative AI, which they hope will facilitate mainframe transformation and help alleviate the skills gap. Not surprisingly, security skills are in high demand because of increasing regulatory compliance requirements, with almost all respondents flagging security as the key factor driving modernization decisions. As a consequence, 77% of organizations in the survey are using external providers to deliver mainframe modernization projects.

Interestingly, respondents identified enterprise-wide observability as critical to effectively leveraging all data across their hybrid IT environment. In fact, 92% of respondents indicated that a single dashboard is important for monitoring their operations, but 85% stated they find it difficult to do this properly.

The survey was carried out by Coleman Parkes Research.

Sometimes I feel a bit like King Knut (Canute) sitting there trying to tell the tide to go back because I am forever telling people that the mainframe is the most modern computing platform currently available. It’s very difficult to modernize something that is already that modern! However, I, of course, recognize that other platforms have advantages in certain areas. I don’t carry a mainframe when I go to a business meeting, I use my very slim laptop. Likewise, cloud computing offers lots of benefits too. But I wouldn’t consider converting my mainframe applications (with all their spaghetti like integration with other applications) to run on a laptop, nor would I want to move them all to the cloud. It’s horses for courses, as they say.

Personally, I like the idea of artificial intelligence, when it can do lots of useful tasks that I may not want to do, and I am not surprised to see mainframe sites embracing its usage. I will be interested to see what the BMC 19^th Annual Mainframe Survey finds about the adoption of AI on mainframes when its results are published this week.

 

A chip off the new block

IBM may not have announced a new mainframe, but it has told us all about the chips that will be powering those mainframes – and it’s very much aimed at making artificial intelligence (AI) software run faster and better.

Let’s take a look at the details.

Back in 2021, we heard about the Telum I processor with its on-chip AI accelerator for inferencing. Now we hear that the Telum II processor has improved AI acceleration and has an IBM Spyre™ Accelerator. We’ll get to see these chips in 2025.

The new chip has been developed using Samsung 5nm technology and has 43 billion transistors. It will feature eight high-performance cores running at 5.5GHz. The Telum II chip will include a 40% increase in on-chip cache capacity, with the virtual L3 and virtual L4 growing to 360MB and 2.88GB respectively. The processor integrates a new data processing unit (DPU) specialized for IO acceleration and the next generation of on-chip AI acceleration. These hardware enhancements are designed to provide significant performance improvements for clients over previous generations.

Because the integrated DPU has to handle tens of thousands of outstanding I/O requests, instead of putting the it behind the PCIe bus, it is coherently connected and has its own L2 cache. IBM says this increases performance and power efficiency. In fact, there are ten 36MB of L2 caches with eight 5.5GHz cores running fixed frequency. The onboard AI accelerator runs at 24 trillion operations per second (TOPS). IBM claims the new DPU offers increased frequency, memory capacity, and an integrated AI accelerator core. This allows it to handle larger and more complex datasets efficiently. In fact, there are ten 36MB of L2 caches with eight 5.5GHz cores running fixed frequency. The onboard AI accelerator runs at 24 tera-operations per second (TOPS).

You might be wondering why AI on a chip is so important. IBM explains that its AI-driven fraud detection solutions are designed to save clients millions of dollars annually.

The compute power of each accelerator is expected to be improved by a factor of 4, reaching that 24 trillion operations per second we just mentioned. Telum II is engineered to enable model runtimes to sit side by side with the most demanding enterprise workloads, while delivering high throughput, low-latency inferencing. Additionally, support for INT8 as a data type has been added to enhance compute capacity and efficiency for applications where INT8 is preferred, thereby enabling the use of newer models.

New compute primitives have also been incorporated to better support large language models within the accelerator. They are designed to support an increasingly broader range of AI models for a comprehensive analysis of both structured and textual data.

IBM has also made system-level enhancements in the processor drawer. These enhancements enable each AI accelerator to accept work from any core in the same drawer to improve the load balancing across all eight of those AI accelerators. This gives each core access to more low-latency AI acceleration, designed for 192 TOPS available when fully configured between all the AI accelerators in the drawer.

Brand new is the IBM Spyre Accelerator, which was jointly developed with IBM Research and IBM Infrastructure development. It is geared toward handling complex AI models and generative AI use cases. The Spyre Accelerator will contain 32 AI accelerator cores that will share a similar architecture to the AI accelerator integrated into the Telum II chip. Multiple IBM Spyre Accelerators can be connected into the I/O Subsystem of IBM Z via PCIe.

The integration of Telum II and Spyre accelerators eliminates the need to transfer data to external GPU-equipped servers, thereby enhancing the mainframe's reliability and security, and can result in a substantial increase in the amount of available acceleration.

Both the IBM Telum II and the Spyre Accelerator are designed to support a broader, larger set of models with what’s called ensemble AI method use cases. Using ensemble AI leverages the strength of multiple AI models to improve overall performance and accuracy of a prediction as compared to individual models.

IBM suggests insurance claims fraud detection as an example of an ensemble AI method. Traditional neural networks are designed to provide an initial risk assessment, and when combined with large language models (LLMs), they are geared to enhance performance and accuracy. Similarly, these ensemble AI techniques can drive advanced detection for suspicious financial activities, supporting compliance with regulatory requirements and mitigating the risk of financial crimes.

The new Telum II processor and IBM Spyre Accelerator are engineered for a broader set of AI use cases to accelerate and deliver on client business outcomes. We look forward to seeing them in the new IBM mainframes next year.

 

Cybersecurity Assistance

There are two areas that I am particularly interested in. They are artificial intelligence (AI) and mainframe security. And IBM has just announced a generative AI Cybersecurity Assistant.

Worryingly, we know that ransomware malware is now available for people to use to attack mainframe sites – that’s for people who may not have a lot of mainframe expertise. It’s totally de-skilled launching a ransomware attack on an organization. We also know from IBM’s Cost of a Data Breach Report 2024 that organizations using AI and automation lowered their average breach costs compared to those not using AI and automation by an average of US$1.8m. In addition, organizations extensively using security AI and automation identified and contained data breaches nearly 100 days faster on average than organizations that didn’t use these technologies at all.

The survey also found that among organizations that stated they used AI and automation extensively, about 27% used AI extensively in each of these categories: prevention, detection, investigation, and response. Roughly 40% used AI technologies at least somewhat.

So that makes IBM’s new product good news for most mainframe sites. Let’s take a more detailed look.

Built on IBM’s watsonx platform, this new GenAI Cybersecurity Assistant for threat detection and response services, enhances alert investigation for IBM Consulting analysts, accelerating threat identification and response. The new capabilities reduce investigation times by 48%, offering historical correlation analysis and an advanced conversational engine to streamline operations.

That means IBM’s managed Threat Detection and Response (TDR) Services utilized by IBM Consulting analysts now has the Cybersecurity Assistant module to accelerate and improve the identification, investigation, and response to critical security threats. The product “can reduce manual investigations and operational tasks for security analysts, empowering them to respond more proactively and precisely to critical threats, and helping to improve overall security posture for client”, according to Mark Hughes, Global Managing Partner of Cybersecurity Services, IBM Consulting.

IBM’s Threat Detection and Response Services is said to be able to automatically escalate or close up to 85% of alerts; and now, by bringing together existing AI and automation capabilities with the new generative AI technologies, IBM’s global security analysts can speed the investigation of the remaining alerts requiring action. As mentioned earlier, the best figure they are quoting for reducing alert investigation times using this new capability is 48% for one client.

Cybersecurity Assistant cross-correlates alerts and enhances insights from SIEM, network, Endpoint Detection and Response (EDR), vulnerability, and telemetry to provide a holistic and integrative threat management approach.

By analysing patterns of historical, client-specific threat activity, security analysts can better comprehend critical threats. Analysts will have access to a timeline view of attack sequences, helping them to better understand the issue and provide more context to investigations. The assistant can automatically recommend actions based on the historical patterns of analysed activity and pre-set confidence levels, which can reduce response times for clients and so reduce the amount of time that attackers are inside an organization’s network. By continuously learning from investigations, the Cybersecurity Assistant’s speed and accuracy is expected to improve over time.

The generative AI conversational engine in the Cybersecurity Assistant provides real-time insights and support on operational tasks to both clients and IBM security analysts. It can respond to requests, such as opening or summarizing tickets, as well as automatically triggering relevant actions, such as running queries, pulling logs, command explanations, or enriching threat intelligence. By explaining complex security events and commands, IBM’s Threat Detection and Response Service can help reduce noise and boost overall security operations centre (SOC) efficiency for clients.

Anything that can accelerate cyber threat investigations and remediation has got to be good, which this product does using historical correlation analysis (discussed above). Its other significant feature is its ability to streamline operational tasks, which it does using its conversational engine (also discussed above).

There really is an arms race between the bad actors and the rest of us. Anything that gives our side an advantage, no matter how briefly that might be for, has got to be good. Plus, it provides a stepping stone to the next advantage that some bright spark will give us. No-one wants their data all over the dark web, and few companies can afford the cost of fines for non-compliance as well as court costs and payments to people whose data is stolen.

The cost of a data breach 2024 – part 2

Last time, we looked at the highlights of IBM’s Cost of a Data Breach Report 2024. We saw that the average cost of a breach was US$4.88m, with the average cost of a malicious insider attack costing US$4.99m. Also, the average time to identify and contain a breach was 258 days, which is lower than previous years, but still a very long time.

This time, I wanted to drill down a bit further into the report. For example, it tells us that AI and automation are transforming the world of cybersecurity. Worryingly, they make it easier than ever for bad actors to create and launch attacks at scale. On the plus side, they also provide defenders with new tools for rapidly identifying threats and automating responses to those threats. The report found these technologies accelerated the work of identifying and containing breaches and reducing costs.

The report also found that the number of organizations that used security AI and automation extensively grew to 31% in this year’s study from 28% last year. Although it’s just a 3-percentage point difference, it represents a 10.7% increase in use. The share of those using AI and automation on a limited basis also grew from 33% to 36%, a 9.1% increase.

The report also found that the more organizations used AI and automation, the lower their average breach costs were. Organizations not using AI and automation had average costs of US$5.72m, while those making extensive use of AI and automation had average costs of US$3.84m, a savings of US$1.8m.

Another plus found by the report was that organizations extensively using security AI and automation identified and contained data breaches nearly 100 days faster on average than organizations that didn’t use these technologies at all.

Among organizations that stated they used AI and automation extensively, about 27% used AI extensively in each of these categories: prevention, detection, investigation, and response. Roughly 40% used AI technologies at least somewhat.

When AI and automation were used extensively in each of those four areas of security, it dramatically lowered average breach costs compared to organizations that didn’t use the technologies in those areas. For example, when organizations used AI and automation extensively for prevention, their average breach cost was US$3.76m. Meanwhile, organizations that didn’t use these tools in prevention saw US$5.98m in costs, a 45.6% difference. Extensive use of AI and automation reduced the average time to investigate data breaches by 33%m and to contain them by 43%.

 

Even after a breach is contained, the work of recovery goes on. For the purposes of the report, recovery meant: business operations are back to normal in areas affected by the breach; organizations have met compliance obligations, such as paying fines; customer confidence and employee trust have been restored; and organizations have put controls, technologies and expertise in place to avoid future data breaches. Only 12% of organizations surveyed said they had fully recovered from their data breaches. Most organizations said they were still working on them.

Among the organizations that had fully recovered, more than three-quarters said they took longer than 100 days. Recovery is a protracted process. Roughly one-third of organizations that had fully recovered said they required more than 150 days to do so. A small share, 3%, of fully recovered organizations were able to do so in less than 50 days.

 

This year’s report found most organizations reported their breaches to regulators or other government agencies. About a third also paid fines. As a result, reporting and paying fines have become common parts of post-breach responses. Most organizations reported the breach within a few days. Over half of organizations reported their data breach in under 72 hours, while 34% took more than 72 hours to report. Just 11% were not required to report the breach at all. More organizations paid higher regulatory fines, with those paying more than US$50,000, rising by 22.7% over last year, and those paying more than US$100,000, rising by 19.5%.

 

About 40% of all breaches involved data distributed across multiple environments, such as public clouds, private clouds, and on premises. Fewer breaches in the study involved data stored solely in a public cloud, private cloud, or on premises. With data becoming more dynamic and active across environments, it’s harder to discover, classify, track, and also secure.

Data breaches solely involving public clouds were the most expensive type of data breach, costing US$5.17m, on average, a 13.1% increase from last year. Breaches involving multiple environments were more common but slightly less expensive than public cloud breaches. On-premises breaches were the least costly.

The more centralized control organizations had over their data, the quicker on average they could identify and contain a breach. Breaches involving data stored solely on premises took an average of 224 days to identify and contain, 23.3% less time than data distributed across environments, which took 283 days. The same pattern of local control and shortened breach life-cycles showed up in the comparison between private cloud architectures and public cloud architectures.

The average cost of a data breach involving shadow data was US$5.27m, 16.2% higher than the average cost without shadow data. Breaches involving shadow data took 26.2% longer on average to identify and 20.2% longer on average to contain than those that didn’t. These increases resulted in data breaches lasting an average lifecycle of 291 days, 24.7% longer than data breaches without shadow data.

While shadow data was found in every type of environment – public and private clouds, on premises and across multiple environments – 25% of breaches involving shadow data were solely on premises. That finding means shadow data isn’t strictly a problem related to cloud storage.

Mega breaches, characterized by more than 1 million compromised records, are relatively rare. The average cost of all mega breach size categories was higher this year than last. The jump was most pronounced for the largest breaches, affecting between 50 million and 60 million records. The average cost increased by 13%, and these breaches were many times more expensive than a typical breach. For even the smallest mega breach – 1 million to 10 million records – the average cost was nearly nine times the global average cost of US$4.88m.

 

Key factors that reduced costs of a data breach included employee training and the use of AI and machine learning insights. Employee training continues to be an essential element in cyber-defence strategies, specifically for detecting and stopping phishing attacks. AI and machine learning insights closely followed in second place.

The top three factors that increased breach costs in this analysis were security system complexity, security skills shortage, and third-party breaches, which can include supply chain breaches.

 

70% of organizations in the study experienced a significant or very significant disruption to business resulting from a breach. Only 1% described their level of disruption as low. The average breach costs were higher when business disruption was greater. Even organizations that reported low levels of disruption incurred average data breach costs of US$4.63m. For organizations that reported very significant disruptions, average costs were 7.9% higher, at US$5.01m.

Most organizations said they planned to increase prices of goods and services following a data breach. 63% of organizations surveyed planned to pass the costs on to customers, a 10.5% increase.

 

This is a report that not only the IT team need to read, but also the chief financial officer because it will be that person who will be responsible for paying company money for the ransom, the fines for lack of compliance, and any court settlements to people whose data has been stolen.

The cost of a data breach 2024

I do seem to be banging on about security recently, but it really is so important. No-one wants to find that their personally identifiable information has been stolen and is currently being shared all over the dark web. And no-one wants to find that their mainframe or other platforms have had all their data stolen and they are looking at massive fines, compensation payments, and loss of customers and future revenue.

But, how do you know exactly how bad things are out there? How do you find out how much it is costing organizations that have been hacked and faced a ransom payment. One answer is the Cost of a Data Breach Report 2024 from IBM. Their headline statistic is that the global average cost of a data breach in 2024 is US$4.88m, which is a 10% increase over last year and the highest total ever. The USA had the highest average data breach cost at US$9.36m. Other countries in top 5 were the Middle East, Germany, Italy, and Benelux (Belgium, the Netherlands, and Luxembourg).

The report also identifies an issue with shadow data, saying that it is involved in 1 in 3 breaches. They suggest that the proliferation of data is making it harder to track and safeguard. Slightly better news is the finding that US$2.22m is the average cost saving for organizations that used security AI and automation extensively in prevention versus those that didn’t.

The USA had the highest average data breach cost at US$9.36m. Other countries in top 5 were the Middle East, Germany, Italy, and Benelux.

Looking in more detail at the report, we find that more than half of breached organizations are facing high levels of security staffing shortages and it’s getting worse. The issue shows a 26.2% increase from the previous year. In cash terms, that corresponded to an average US$1.76m more in breach costs. The report goes on to say that even as 1 in 5 organizations say they used some form of gen AI security tools, which are expected to help close the gap by boosting productivity and efficiency, this skills gap remains a challenge.

Many organizations trust their all the employees, and yet the report says that the average cost of a malicious insider attack is now US$4.99m. The report says that compared to other vectors, malicious insider attacks resulted in the highest costs but were only 7% of all breach pathways. Other expensive attack vectors were business email compromise, phishing, social engineering, and stolen or compromised credentials.

Phishing and stolen or compromised credentials ranked among the top 4 costliest incident types. Compromised credentials topped initial attack vectors. Using compromised credentials benefited attackers in 16% of breaches. Compromised credential attacks can also be costly for organizations, accounting for an average US$4.81m per breach. Phishing came in a close second, at 15% of attack vectors, but in the end cost more, at US$4.88m. Gen AI may be playing a role in creating some of these phishing attacks. For example, gen AI makes it easier than ever for even non-English speakers to produce grammatically correct and plausible phishing messages.

Watching TV and movies might make you think that breaches are usually discovered fairly promptly and dealt with the next day. Sadly, the report found that breaches involving stolen or compromised credentials took the longest to identify and contain of any attack vector. That was 292 days. Similar attacks that involved taking advantage of employees and employee access also took a long time to resolve. For example, phishing attacks lasted an average of 261 days, while social engineering attacks took an average of 257 days.

The good news is that the average time to identify and contain a breach fell to 258 days, reaching a 7-year low, compared to 277 days last year. The report points out that this global average of mean time to identify (MTTI) (194 days) and mean time to contain (MTTC) (64 days) excludes Benelux because, as a new region in the study, it was having outsized influence and skewed results much more than the average.

Ransomware victims that involved law enforcement ended up lowering the cost of the breach by an average of nearly US$1m, although that excludes the cost of any ransom paid. Involving law enforcement also helped shorten the time required to identify and contain breaches from 297 days to 281 days.

The industrial sector experienced the costliest increase of any industry, rising by an average US$830,000 per breach over last year. This cost spike could reflect the need for industrial organizations to prepare for a more rapid response, because organizations in this sector are highly sensitive to operational downtime. However, the time to identify and contain a data breach at industrial organizations was above the median industry, at 199 days to identify and 73 days to contain.

Healthcare is still the costliest in terms of a data breach at US$9.77m, but that was down from US$10.93 in 2023. Financial is the second costliest sector at US$6.08m this year, with Industrial third with an average cost of US$5.56m.

Nearly half of all breaches (46%) involved customer personal identifiable information (PII), which can include tax identification (ID) numbers, emails, phone numbers, and home addresses. Intellectual property (IP) records came in a close second (43% of breaches). The cost of IP records jumped considerably from last year, to US$173 per record in this year’s study from US$156 per record in last year’s report.

The costs from lost business and post-breach response rose nearly 11% over the previous year, which contributed to the significant rise in overall breach costs. Lost business costs include revenue loss due to system downtime, and the cost of lost customers and reputation damage. Post-breach costs can include the expense of setting up call centres and credit monitoring services for impacted customers, and paying regulatory fines.

Worryingly, 45% of all breaches were caused by IT failures or human error. The breakdown is 23% are due to IT failure and 22% are due to human error.

Interestingly, security teams and their tools detected breaches 42% of the time. Benign third parties detected the breach 34% of the time, and attackers themselves identified the breach 24% of the time. Security teams are getting better at discovering breaches because the 2023 figure for identification was 33% of the time. When a breach was disclosed by an attacker, the average cost was US$5.53m. However, when a security team identified a breach, the average cost was US$4.55m.

Even so, no-one can be complacent. It’s still taking a long time to detect a breach. It’s still costing companies a lot of money. More needs to be done to protect individual’s data, don’t you think?

It’s a really useful report by the Ponemon Institute for IBM.

There will be more details from the report next time.