If you've worked in cybersecurity for as long as I have, then you'll know there are a couple of things you can count on. First, the threats that are out there never stop evolving. And second, sooner or later, you're going to be in the bullseye.

What makes life so much harder today is that AI and other automated tools have dramatically narrowed the gap between vulnerability discovery and the time it takes to exploit them.

And when this can now be measured in minutes – seconds, even – you know you have a problem. This fundamental change in the way adversaries operate means we no longer have the luxury of time to understand an attack, assess the risk and decide what to do next.

Which means we have to be better prepared and have resiliency for whatever is thrown at us.

Visibility is key

For me, that starts with accepting a simple reality: you cannot defend what you cannot see. And it’s why visibility is one of the most important capabilities an organization can develop.

After all, if you understand what exists within your environment – how those systems interact and what normal looks like – then you're in a much stronger position to identify unusual behavior before it develops into something more serious.

Observability, on the other hand, takes that visibility to the next level. It provides the context security teams need to make informed decisions quickly, especially when time is working against them.

In other words, visibility tells you what is happening, while observability helps you understand why it's happening.

And that’s crucial. Today's organizations operate across on-premises infrastructure, cloud environments, networks, and an increasing number of connected technologies.

As those environments become more distributed, understanding what's happening across them becomes significantly harder.

Without that visibility, it's difficult to understand where your risks are, how systems interact, or where an attacker may be able to exploit a weakness.

Think like a hacker

Which leads me neatly onto my next point. Throughout my career, including my time working in offensive cyber operations in the intelligence community, I've found that the most effective way to understand risk is to think like the adversary.

I start by asking how someone would attack an organization and then work backwards to identify and close gaps.

That’s because attackers don't see organizations in the way that you or I might do. They’re always on the hunt for a toehold in. They look for weaknesses in people, processes and technologies.

They look for the easiest route first to achieve their objective. And then they exploit that weakness.

And it’s an approach I would urge all security leaders to adopt if they want to stay one step ahead.

That means continuously asking where an attacker would start, how they would move through the organization and what controls would slow them down or stop them altogether.

But for this to work, it also requires organizations to design resilience into the way they operate. And that’s something we’ve embedded across our organization.

For instance, we have internal and external teams that conduct continuous product, enterprise, spear-phishing and physical penetration testing.

For us, it's about educating the team across the business to ensure they remain vigilant. But it’s also about inoculating people so that when they see something suspicious online, they have that instinct that something might be wrong and they report it.

We also want to make it easy for people to report events so we can analyze them quickly and better understand the targeting.

Secure by design

We’ve also invested heavily in Secure by Design to ensure that all the products we deliver to customers are as secure as humanly possible. In practice, it means being able to trace every piece of code back to its source and verify its integrity throughout the development process.

It's similar to maintaining a chain of custody for evidence. We want to know exactly where software components come from, how they're verified and how they're protected throughout the entire build process.

More broadly, Secure by Design is increasingly being adopted across our industry as organizations recognize the importance of software integrity, traceability and transparency throughout the development lifecycle.

This is important because, as I said at the beginning, there are two certainties in cybersecurity: threats will continue to evolve, and organizations will continue to be targeted. Businesses across the world must adapt quickly to the grim reality that a cybersecurity incident isn’t a matter of if, but a matter of when. And AI is supercharging the pace at which all this is happening and broadening the blast radius of any attack.

That’s why you need to understand your environment well enough to reduce unnecessary risk, detect malicious activity quickly and limit the blast radius when something does happen. Pair that with a clearly defined and tested plan for recovery and that's what robust cyber resilience looks like in practice.

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Defense is operating in an environment where the pace of change continues to increase. Adversaries are adapting quickly and technology development cycles are becoming shorter. The boundaries between physical and digital operations are also becoming harder to define, while military commanders have more information available to them than ever before.

This changes how operational advantage is achieved. The performance of an individual platform or system remains important, but so does its ability to work effectively within the wider operational environment. Information needs to move securely to where it is needed, supporting decisions and action across different domains.

As new technologies are introduced, integration will become an increasingly important part of defense capability. The challenge is making sure innovation can be put to practical use alongside the systems and infrastructure already supporting operations.

Connecting technology across defence

Conversations around defense innovation often focus on AI, autonomous systems, advanced sensors, cyber capability and space assets. Each has a significant role to play, but none operates in isolation.

Information gathered by one system may need to be shared across multiple domains before it supports an operational decision. Networks, data, command systems and people all contribute to that process. The value of any individual technology is linked to how effectively it connects with the wider operational environment.

This principle also applies to the infrastructure supporting military operations. Communications networks, operational facilities and digital systems all contribute to creating an environment where information can move securely and reliably. As these environments evolve, resilience and security must be designed from the outset though approaches such as secure-by-design and zero-trust principles.

Strengthening the foundations of operational capability

AI has become one of the defining topics in defense. Its ability to process information and support decision-making has significant potential, but those capabilities depend on the quality of the data available and the resilience of the infrastructure that carries it.

Reliable communications, trusted data and secure networks remain fundamental to operational effectiveness. If those foundations are unavailable or compromised, the benefits of advanced technologies are reduced.

Therefore, creating decision advantage is not simply a technology challenge. It is an infrastructure and digital challenge and increasingly, a collaboration challenge.

For organizations supporting critical infrastructure, this has become an increasingly familiar challenge. Communications, operational technology, and digital infrastructure must work together to create environments where reliability cannot be compromised.

Keeping people at the heart of automation

Automation is attracting considerable attention across defense as organizations are looking to improve efficiency and increase operational tempo. However, automation should never be viewed as an end.

Its greatest value often comes from reducing routine activity rather than replacing people. Predictive maintenance, autonomous monitoring, automated network management and logistics optimization all help reduce the time spent on repetitive tasks, allowing highly trained personnel to focus on areas where experience and judgement remain essential.

The most effective technologies do not replace human capability - they amplify it.

The infrastructure supporting multi-domain operations

As operations become increasingly integrated across land, sea, air, cyber and space, infrastructure is taking on greater strategic importance. Communications, transport, energy, and digital systems all contribute to operational capability, showing how infrastructure and technology are becoming increasingly interdependent.

The movement of people, information, energy, and capability all contribute to operational readiness. Reliable infrastructure enables those elements to function as a single system, ensuring capability can be delivered when and where it is needed.

One example can be seen in the Falkland Islands, where runway infrastructure forms part of maintaining long-term strategic capability and readiness. It illustrates how infrastructure and operational capability are becoming increasingly interconnected.

Bringing innovation into operational use

The UK benefits from an established community of innovators, with government, industry, academia, SMEs and the Armed Forces all contributing to the development of new ideas and technologies. The opportunity now is to ensure those innovations can be adopted enough to meet operational needs.

Collaboration is still a critical part of this process. Bringing together different perspectives helps ensure technology is developed with practical application in mind and can be integrated more effectively into future capability.

Delivering the next phase of defense capability

Much of the technology required to support future defense operations already exists. The focus now needs to be on how quickly it can be integrated and put to operational use, giving the Armed Forces the advantage they need as threats and operating environments continue to change. That requires stronger connections across networks, data, platforms, people and infrastructure.

Collaboration between government, industry, academia, SMEs and the Armed Forces will remain central to moving capability from development into deployment. Technologies also need a clearer and faster route beyond demonstrations and pilots, so useful capability reaches operators when it is needed.

The organizations that succeed will be those able to bring people and technology together across the wider defense environment. Doing that securely, reliably and at pace will determine how effectively innovation translates into operational advantage.

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• Meta wants to upgrade ex-managers back to managers
• 7,000 workers were moved into the Applied AI org earlier this year
• Company's CTO admitted previous workforce changes were "atrocious"

Meta has reportedly partially reversed its decision to push for flatter, manager-light teams by asking some employees in its Applied AI business to move from individual contributor roles back into managerial positions.

New Business Insider reporting suggests Meta is asking certain workers to volunteer themselves as managers rather than forcing them into it, but without the company sharing comment on the matter, it's unclear how workers who refuse may be impacted.

As for the business itself, it's a pretty new one that was established this year, with around 7,000 employees shifted under the Applied AI umbrella.

Meta wanted fewer managers, now it wants more

Some of the workers who were previously assigned as managers were reassigned as individual contributors when they joined the Applied AI org, with Business Insider previously reporting how many workers felt they'd effectively been "drafted" into the business, highlighting ongoing friction internally.

The manager-light stance comes from CEO Mark Zuckerberg's 'Year of Efficiency' announcement in 2023, when he argued that a flatter organization could be more nimble and better poised for change.

With the company as a whole now on track to spend over $130 billion this year on AI chips and infrastructure, it's possible that a growing Applied AI organization could be behind the need for more traditional management.

Still, Zuckerberg's efficiency-driven, manager-light program seems to remain in force across other areas of the business, with this push for new managers looking to only be affecting Applied AI.

Separately, CTO Andrew Bosworth previously admitted that Meta's rollout of its new AI division was "atrocious" (via Wired), promising better communication for future changes.

"We shook up the management structure that was providing you stability while rapid changes in strategy, including the boom/bust cycle of hiring, left entire teams in the lurch," he admitted.

AI has rewritten the enterprise data playbook. Workflows no longer just create temporary operational data, but vast amounts of high-value assets, from LLM training datasets and model outputs to logs, metadata and archived knowledge that may need to be preserved for years.

As enterprise tech leaders seek to scale infrastructure to meet these demands, storage requirements are undergoing a fundamental shift. Capacity and performance remain critical, but data security has become equally important. Today, long-term data integrity and absolute cyber resilience carry equal weight.

Protecting this data, however, is no longer just about defeating today’s threat vectors. It requires preparing storage infrastructure for a significant shift: the arrival of quantum computing.

Data is a long-term strategic asset, not a short-lived trend

AI is accelerating data growth, but it can also extend the useful life of information. Data recorded today will be harvested for compliance, advanced analytics and model retraining for years to come.

To manage this economically, enterprise architectures rely heavily on high-capacity HDDs. While flash technologies dominate performance-critical hot tiers, HDDs remain the undisputed backbone of large-scale storage, providing the capacity, economics and longevity needed to archive data at scale.

As a result, organizations must consider how to protect not only today's data, but also its future value. After all, if the underlying infrastructure is compromised down the road, the very assets driving future AI innovations become the biggest operational and regulatory liability.

Harvest now, decrypt later

Current encryption technologies remain effective against conventional threats. However, quantum computing is expected to challenge some of the cryptographic methods used for authentication and key exchange.

This has led to concerns around “harvest now, decrypt later” attacks, where encrypted data is collected today with the expectation that future quantum capabilities could potentially decrypt it later.

For organizations storing sensitive intellectual property, research data or AI training datasets, this means security decisions made today could have implications for years to come.

Preparing for that future requires action from security leaders and IT directors now.

Security must be built into the infrastructure

Security is often viewed through the lens of data encryption, and with good reason. Self-encrypting drives (SEDs) provide always-on, hardware-based AES-256 encryption that helps protect data at rest without impacting performance.

But protecting data alone is no longer enough.

Storage devices themselves must be trusted. Firmware, authentication mechanisms, provisioning processes and diagnostic tools all play a role in ensuring a drive operates securely throughout its lifecycle.

If attackers compromise a device's firmware or trust architecture, broader security controls can be undermined regardless of how data is encrypted elsewhere in the system. This makes storage security a critical component of overall cyber resilience.

Implementing quantum-resistant defenses in storage

To counter these emerging attack vectors, the storage industry is actively embedding post-quantum cryptography into hardware architecture of enterprise hard drives. Rather than focusing solely on protecting data, the objective is to protect the trust architecture that underpins the drive itself.

Post quantum cryptography (PQC) technologies are being incorporated into areas such as secure key establishment, firmware authentication, secure provisioning, and trusted diagnostics. These capabilities are designed in alignment with established NIST post-quantum standards and are implemented using hybrid approaches that combine classical cryptography with quantum-resistant algorithms.

In practical terms, this means that the mechanisms responsible for establishing trust, validating firmware integrity and protecting administrative functions can remain resilient against both conventional and future quantum-enabled attacks. With the operational service life of HDDs often spanning 5 years (or more), implementing PQC today helps protect against quantum-based threats that may not materialize for several years, but that we know are coming.

Importantly, HDDs have long incorporated security controls to defend against today's threats. PQC does not replace these protections; it enhances them by adding an additional layer of resilience against emerging attack vectors.

Trust in the AI era

For many years, storage innovation was primarily defined by increases in capacity. Today, the expectations placed on infrastructure are much broader.

Organizations seek storage platforms that can scale with AI-driven data growth, deliver reliable performance, preserve integrity over long retention periods and withstand an increasingly complex threat environment.

PQC represents an important step in that evolution. By extending protection beyond data encryption and into the trust mechanisms that underpin storage devices themselves, PQC-enabled HDDs help organizations prepare for the security challenges of tomorrow while protecting the data they manage today.

As AI continues to elevate the strategic value of enterprise data, security can no longer be a short-term, reactive consideration. Trust must be engineered directly into the hardware layer and built to outlast the threats of today, tomorrow and the quantum era ahead of us.

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• OpenAI for Financial Services comes with built-in premium datasets
• Third-party subscription sharing is also in the works
• GPT-6 promises 100% on the 256-512K Long Context benchmark

OpenAI has lifted the wraps off ChatGPT for Financial Services, its latest industry-specific version of ChatGPT Work designed to give sector professionals access to relevant reasoning and models.

The company warned that analysts regularly spend too much time finding data, checking figures, building models and turning complex analysis into presentation-friendly reports – all things it hopes to be able to tackle with this iteration of ChatGPT Work.

Core to this new product is, of course, GPT-6 Astra, but OpenAI also worked with investment experts from Morgan Stanley and Evercore to guide its development.

ChatGPT for Financial Services

With the launch of ChatGPT for Financial Services, OpenAI has built premium datasets from the likes of Daloopa, PitchBook and LSEG News directly into the tool, so users won't need to negotiate their own external datasets. By hosting and indexing that data itself, OpenAI can ultimately improve performance and latency for end users.

OpenAI also promises to be working with S&P Capital IQ, LSEG, MSCI, Dow Jones Factiva and Moody’s so that users can access their subscription benefits directly through their ChatGPT login to reduce the friction further.

All of this with the benefits of OpenAI's latest frontier model, Astra, which sees marked improvements across the board. The jumps from 91.5% to 100% at 256K-512K and from 73.8% to 96.3% at 512K-1M are especially noteworthy on the Long Context MRCR benchmarks when compared with GPT-5.6 Sol, because finance work often requires handling long documents like annual reports and filings.

The company also advertises a 100% score for the ExploitBench cybersecurity benchmark, which should come as welcome news to this highly-regulated industry.

Only eligible financial institutions will gain access to the tool for now, and they must contact OpenAI for options.

"ChatGPT for Financial Services is one way we serve customers across the industry, but we recognize that it will require a range of solutions to address the needs of the finance industry," the company said in its launch blog post.

"OpenAI has a long history of collaborating with innovators to unlock novel AI solutions. Financial services firms and developers can use our API to build specialized applications for the needs they understand best. OpenAI brings frontier models and the capabilities to put them to work. Financial institutions, data providers, and software partners bring specialized expertise, trusted information, and customer relationships."

The vast majority of enterprise leaders are bullish about how ready their organizations are for AI-generated code. However, once that code reaches production, this confidence wavers as incidents arise. This pattern shows up across multiple independent studies in this year alone.

For instance, data published in April 2026 found that monthly production incidents climbed by almost 58% as AI coding tools scaled across engineering teams. A similar study from June found that the same volume of code changes is now producing more than three times the production incidents it did before AI coding tools were introduced en masse.

These findings are echoed in the 2026 State of Code Abundance Report, which surveyed more than 200 enterprise technology leaders and found that 92% expressed confidence in the production readiness of AI-generated code and rated their own AI-code readiness at an average of 84 out of 100.

Yet, the same study found that 81% reported an increase in production issues tied to AI-generated code - indicating a significant gap between confidence and control. While 93% say they have a formal process for reviewing and releasing AI-generated code into production, only 56% report that those processes are always enforced.

Furthermore, 86% of the same respondents report full or high visibility into AI-generated code, signaling a major contradiction - high visibility and rising incidents cannot both be describing the same pipeline.

Understanding the visibility gap

This is a familiar phenomenon in business, where confidence tends to be highest in areas where organizations have the least ability to measure their own performance. These enterprises aren't lying about their trust in AI-generated code, they believe it is production ready. The issue is that belief has out-grown the instrumentation needed to verify it.

We need to remember that AI coding tools are, by most measures, doing exactly what they were built to do: allowing more code to be produced faster and shifting engineering effort from writing code to deciding what should ship. Prior to this, the amount of code an organization could produce was largely tied to the size of its development team, incurring significant constraints for many.

In the agentic era, this barrier has effectively disappeared. What hasn't been adjusted is understanding what that code does once it's live, who wrote it, why it changed, and what broke when it did. Most organizations could stay on top of this governance while code was being written at human speed, but the challenge now is keeping up with the pace of agentic coding.

AI has widened a visibility gap that already existed, at a pace most governance structures were never designed to keep up with. For enterprise leaders right now, the natural instinct is to estimate how much faster AI can make their teams. This thinking leads many organizations to fall into the trap of prioritizing speed over quality, which leads to more errors when code is deployed - causing the process to slow dramatically.

The importance of code governance

Before investing heavily in AI coding tools, the best thing to establish is an idea of how much of your current pipeline you can actually see, measure and attribute. As only 12% of organizations have a dedicated team for governing AI-generated code, the vast majority of enterprises adopting these tools are doing so without a designated owner for the risk they're taking on.

This means that when something goes wrong, there's frequently no clean way to trace it back to a decision, model, or person accountable for the outcome.

This is the part of the pipeline that doesn't get enough attention, because it's less exciting than the productivity headlines. However, it's the part that will determine which organizations actually reap the benefits of agentic coding and which ones spend their time and resources cleaning up after it.

There’s a temptation, which is understandable given the competitive pressure, to treat AI-driven code generation as a race: whoever ships the most, fastest, wins. This is the wrong way to think about it, and the winners will actually be the ones that pause and strengthen their governance before they accelerate.

Control vs playing catch-up

The organizations that will benefit from this shift are the ones building measurement, attribution and oversight into their pipelines ahead of time. That means treating governance as infrastructure rather than paperwork and being able to answer, at any point, which parts of their codebase were AI-generated, who reviewed them, and what production behavior they're responsible for.

Furthermore, budget owners must be able to say what they're actually spending on AI-assisted development, rather than estimating.

None of this slows delivery down in the long term, and if anything, it's what allows delivery to keep accelerating without the incident curve growing alongside it. The gap between how confident enterprises feel about AI-generated code and how much of it they can actually see isn't going to close on its own. It will close because leadership teams decide to build the visibility first.

The organizations that do that now, while the rest of the industry is still counting lines of code shipped, are the ones that will still be standing when the next wave of AI-driven development arrives.

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• Nearly two-thirds of AI users aged 18-28 want clearer instructions from their bosses
• Nearly half of the same cohort have difficulty explaining work completed with the help of AI
• The figures could indicate a brand new challenge to management

“Tell me exactly what you want me to do” – that seems to be the message from older millennial and younger Gen Z workers, who have expressed a need for highly specific instructions from their bosses.

Research has found that 62% of the 18-28 age group who regularly use AI want their bosses to give them the same sort of clear steps and parameters as one might give an LLM chatbot.

While a cohort of workers who are attentive to the needs of the business might sound good, deeper investigation may prompt you to think again. The study by Use.AI implies a deeper-seated issue that points to a management challenge unlike anything seen before on this scale.

Detailed steps

Use.AI surveyed 11,742 adults based in United States, the United Kingdom, Canada, the European Union, Australia and Latin America, finding 62% of the 18-28 age group familiar with the AI use are accompanied by 41% of those aged 40 and older who also want more detail about tasks from their line managers.

Why do younger users need clearer, more detailed steps? It could be a simple matter of judgement, with the 18-28 age group requiring more experience in making their own decisions. But with the presence of AI in their working lives, could they be fearful of being replaced?

The report also explores a slightly different dimension. Within the 18-28 age group, 44% say they have had difficulty to explain work created with AI assistance. This raises a key concern: does the employee even know what their tasks are supposed to achieve? For that matter, does the business?

Sound judgement

AI has changed businesses considerably, and made an impact that doesn’t only improve productivity. As Ihor Herasymov, Co-Founder & CEO of Use.AI, observes, “AI is exposing which parts of work were never really about producing the answer. When a model can generate the output in seconds, the value of some tasks shifts to whether the person can make a sound judgment and stand behind it.”

Other results from the survey support this. With 57% of the 18-28 age group of regular AI users stating that they are less likely to memorize information that can be collected via AI, and 53% of the same group frustrated by “ordinary” tasks that could be completed by a chatbot, a whole new workplace dynamic is being quietly established.

Herasymov concludes, “Managers now have to be much clearer about whether they need the result or whether the task itself is meant to develop the employee.”