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.