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Being offensive in your defense

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These days, it’s not good enough to get the alert. The speed and sophistication of today’s attackers along with the growing number of insider-driven and data-handling risks demand that we find issues even before traditional detections trigger. Modern threats increasingly blend into normal user behaviour, especially when the activity involves sensitive data moving across cloud apps, browsers, and collaboration tools.  

AI-powered attackers and AI-enabled users are forcing defenders to rethink their approach. As we “shift left,” even the acceptable timeframe for discovering potential data exposure is shrinking. By the time a detection triggers, the data may have already been copied or shared outside approved channels. 

This is why proactive threat hunting is evolving into an essential discipline. In many organisations, that means focusing on the early indicators of data misuse, not just system compromise. Advancements in AI and automation now make it far more practical for security teams to identify patterns of risky data movement long before they escalate into incidents. 

In this article, I’ll explore the growing need for proactive threat hunting, the risks of relying on reactive alerts in 2026, and how modern capabilities are helping organisations pivot from being the hunted to becoming the hunter when it comes to protecting their most sensitive information. 

Explaining the Industry Shift Towards Proactive Threat Hunting

Rapid, AI-powered attacks are getting increasingly buried within legitimate business workflows. By the time SOCs or security teams receive a rule-based alert, a user may have already synced classified files to an unmanaged cloud drive, copied sensitive content into an AI tool, or moved high-value data in a way that appears benign on the surface.

These types of low-and-slow actions often evade traditional detection because they mimic normal productivity. Insider misuse and subtle forms of data leakage rarely trigger the same type of signatures as malware or command-and-control traffic. Even AI-driven social engineering attacks now create downstream data risks without necessarily involving a malicious link or attachment. 

Catching these scenarios requires looking directly at data interactions, not just at system events. Proactive threat hunting shifts the focus to understanding how, where, and why sensitive data is being accessed or moved and whether that behaviour aligns with what is expected.

For that, you need a certain set of skills.

Threat Hunting: Skills Required

The role of threat hunter has always been a hybrid one, combining technical expertise with strong analytical instincts. In a data-centric context, that combination becomes even more important.

Threat hunters must know how to gather and interpret telemetry related to data handling, such as file access, classification tags, transfer paths, browser activity, cloud sync behaviour, and anomalies in user behaviour patterns. They need to understand not only the technology but also the organisation’s workflows, so they can distinguish legitimate use from subtle misuse.  

The best hunters pick up on patterns: unusual volumes of data movement, access outside normal working hours, files moving to new destinations, or slowly escalating behaviours that wouldn’t trigger a single alert on their own. There’s still a human element of gut instinct and puzzle-solving, but now it’s applied to data behaviour rather than purely system-level indicators.  

How Much Automation and Agentic AI in Threat Hunting?

A lot, and more every day. Fortunately, automation and AI are stepping in to close the skills gap and augment human analysts, especially within data protection workflows. 

AI-powered threat hunting doesn’t replace expertise; it amplifies it. Think of it as a mech suit for data security teams. Many of the foundational tasks like collecting telemetry, enriching events, and correlating user actions across applications are already automated. Agentic AI systems can now evaluate data movement patterns, identify anomalies, and highlight situations that warrant closer human review.

Advancements in analytics, machine learning and threat intelligence are accelerating this trend, further improving the execution of autonomous threat hunting and helping teams surface early indicators of risky data behaviour with greater speed and accuracy.

What we’ll see going forward is an even tighter pairing between AI and human judgement. AI handles scale and pattern recognition; humans bring context, business understanding, and the ability to make nuanced decisions about risk.

Weighing the Benefits of Threat Hunting for Your Team

For many companies, proactive threat hunting may seem like a luxury reserved for the largest and most mature security programs. But the benefits, especially in a data-centric world, increasingly outweigh the costs.

There are certainly up-front investments: gaining visibility into data movement, deploying AI-enhanced tools, and ensuring the right people can interpret the signals. There are operational costs as well, such as maintaining policies, managing alerts, and training analysts to understand data behaviour.  

But the benefits are substantial. Attackers, insiders, and even well-meaning employees are increasingly operating below the threshold of traditional detections. Proactive threat hunting helps uncover these subtle patterns early, before sensitive information is exposed or exfiltrated. Many organisations are adopting specialised email and cloud-security controls for exactly this reason: reactive tools simply cannot keep up with the sophistication and subtlety of modern data risks.

By identifying issues closer to their origin point, security teams can minimise the potential impact or even avoid harm altogether. 

Conclusion

Attackers and users are interacting with data in ways that continue to evade traditional detection tools. In some ways, this is a testament to how effective we’ve become at catching the obvious threats. But it also means our strategies must evolve to stay ahead of the quieter, more nuanced risks that centre on data. 

Proactive threat hunting supports this shift. Whether focused on system compromise or on the behaviours that place data at risk, the principle remains the same: the best defence is a good offence. Understanding how sensitive information is accessed and moved allows security teams to act earlier, faster, and with far greater clarity.

And in today’s environment, that difference is often what determines whether an incident becomes a headline or just another day of good defence.



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Financial planning firms boost technology spending

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KAREN JOY BACUDO

Finance Editor

Financial planning firms are increasing investment in technology, according to new research from Saltus Partnership Programme and L.E.K. Consulting. The study found 42% of firms are using technology investment to manage growth and regulatory demands.

That is up from 35% in the previous survey and reflects a broader shift in how firms allocate resources amid rising compliance pressures and day-to-day operational demands.

The Financial Planning Growth Index is based on a survey of 216 senior figures at financial planning firms of different sizes. Conducted before the recent escalation in geopolitical tensions, it also found broad confidence in business performance, with 74% of respondents saying they were confident about increasing revenues this year.

That confidence appears to be feeding into spending plans. A quarter of firms said improving operational efficiency was among their top three business priorities over the next one to three years, up from 22% in the previous survey, while 8% named digital transformation as a key priority.

The data suggest firms are focusing less on headline technology projects and more on practical changes to systems and workflows. Over the next one to three years, 34% said they planned to upgrade existing systems, while 24% intended to introduce new financial planning tools.

Smaller shares identified more specific areas for investment. Some 8% said they planned to invest in data analytics, and 7% were considering launching their own app or client portal.

The research also pointed to changes in how firms are organising work as they expand. Around 22% said they were spending less time on sector events, while 20% reported increasing their use of paraplanners.

These shifts suggest advisers are trying to devote more time to serving clients and managing business growth, rather than relying on traditional networking. They also show that firms are considering technology investment alongside changes to staffing and operating models, rather than in isolation.

Investment focus

For many firms, the emphasis appears to be on replacing or improving core systems already in place, rather than making large-scale bets on entirely new digital services. Upgrades to existing platforms were the most widely cited area of planned spending, pointing to a market still dealing with legacy processes and fragmented systems.

New planning tools ranked second, indicating that firms are also looking at software that could affect how advisers assess client needs, prepare recommendations and manage ongoing relationships. The lower figures for analytics and client-facing apps suggest that internal processes remain the more immediate concern for most respondents.

The findings come as financial planning firms face pressure to grow revenue while meeting tighter expectations around governance, documentation and oversight. Firms are increasingly presenting technology spending as a way to protect margins while handling those demands.

“Our research uncovers a clear direction of travel when it comes to investment in technology. We have seen first hand how modern technology can enhance the delivery of financial advice, to the benefit of firms and clients alike, and it is encouraging to see that so many firms are not only planning to increase their investment, but also have a clear strategy for doing so. This is testament to the resilience of the sector, which continues to demonstrate its ability to innovate and provide the best possible service to clients,” said Nick Heath, Head of Relationship Management at the Saltus Partnership Programme.

Strategic shift

The pattern of responses indicates that firms are becoming more deliberate about where and why they spend on technology. Instead of viewing digital tools as a separate workstream, many appear to tie investment decisions to profitability, workflow efficiency, and regulatory requirements.

That matters in a market where firms vary widely in size, ownership structure and operational maturity. Larger groups may have more room to fund upgrades or roll out new systems, while smaller firms often need to be selective about where spending will have the quickest operational effect.

“As the speed at which technology is advancing shows no sign of slowing, firms of all sizes cannot afford to be left behind. We are witnessing an important shift, however, where firms are treating investment in technology more strategically. By taking a long-term view, assessing the full suite of options available and setting clear guardrails, firms will be best placed to navigate the transition and unlock tangible value,” Bronswe Cheung, Partner at L.E.K. Consulting, said.



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UK firms boost cyber & AI spending, Barclays survey

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UK businesses are increasing spending on cybersecurity and artificial intelligence, with cyber, cloud, and AI accounting for 44% of planned technology budgets over the next year, according to Barclays’ latest survey.

The findings suggest a shift in priorities as companies weigh efficiency gains from new tools against rising operational and security risks. Some 68% of UK business leaders expect to increase cybersecurity investment over the next 12 months, while 46% believe that adopting new technologies is increasing their exposure to cyber threats.

Confidence in cyber preparedness remains uneven. Fewer than three in 10 businesses (29%) said they were confident in their ability to respond to a major cyber incident, despite 82% saying their cybersecurity measures are keeping pace with technology adoption.

Spending patterns differ sharply by size. Average cybersecurity spending so far this year stands at £505,000, rising to £1.3 million for large businesses and falling to £134,000 for small businesses and £15,000 for micro businesses.

Large companies have also moved faster to raise cyber budgets. More than a third of large firms (36%) have increased cybersecurity investment since the start of the year, compared with 26% of smaller businesses and 4% of micro businesses.

Risk and response

Among businesses concerned about the impact of a serious cyber incident, the most common worry was damage to customer trust and confidence, cited by 28%. That was followed by operational disruption or downtime at 27% and revenue loss at 26%. Among large businesses, the leading concern was the loss of sensitive data or intellectual property, mentioned by 33%.

The survey suggests businesses are trying to balance investment in new digital tools with tighter risk controls. While many respondents said they were pressing ahead with AI and automation, concerns about reliability, data security and cost remain widespread.

More than half of businesses (52%) said AI and automation had improved productivity. Respondents reported spending less time on administrative tasks (38%), making decisions faster (34%), and spending more time on higher-value work (31%).

Use of agentic AI has also spread, with 61% of businesses now proactively using it in their operations, suggesting adoption has moved beyond limited trials in many organisations.

AI priorities

Planned AI use over the next two years spans a broad range of business functions. Data analysis and forecasting topped the list at 38%, followed by the automation of administrative work to improve employee productivity at 31%. Enhancing customer experience and strengthening cybersecurity were each cited by 29%.

Smaller companies showed a different set of priorities. More than a third of small businesses, or 34%, said they planned to use AI to reduce operational costs, while nearly half of micro businesses, or 46%, said they had no plans to use the technology.

Reservations about AI remain notable even as adoption grows. More than a quarter of respondents (26%) cited concerns about the accuracy and reliability of AI outputs. Data security, cybersecurity risks and implementation costs were each mentioned by 24%.

Matt Hammerstein, Chief Executive of Barclays UK Corporate Bank, linked the investment trend to a tougher trading environment for companies.

“UK businesses are now operating in an environment where uncertainty has become the norm. Geopolitical instability and persistently high costs are feeding directly into cash flows, borrowing decisions and investment plans,” said Hammerstein.

“What’s striking, however, is how businesses are responding. Rather than pulling back entirely, many are adapting to this new reality by tightening financial discipline, managing cash carefully and prioritising investment where it strengthens resilience, productivity and long-term competitiveness,” he added.

Barclays said the data also reflected differing pressures across the business landscape, with larger companies more willing to commit to longer-term borrowing while smaller companies focus on liquidity and day-to-day financial management.

“SMEs are navigating higher costs and ongoing uncertainty, which continues to weigh on day-to-day decisions. While larger firms push ahead with longer-term borrowing, many smaller businesses are focused on building cash buffers and closely managing their financial position. At the same time, AI is starting to present tangible opportunities for SMEs, particularly where it can help improve productivity and make everyday tasks more efficient,” Abdul Qureshi, Head of Barclays Business Banking, said.

The survey was based on research among 1,000 senior business decision-makers across micro, small, medium and large UK businesses, alongside separate research among 500 business-to-business leaders. One of its clearest findings was that investment in cyber resilience is no longer treated separately from digital transformation but as part of the same spending decision, with cloud, cyber, and AI accounting for almost half of planned technology budgets.



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Improbable backs Otomato with USD $2 million DeFi bet

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KAREN JOY BACUDO

Finance Editor

Improbable has invested USD $2 million in decentralised finance monitoring company Otomato, in one of the first external investments tied to its current focus on AI and web3 companies.

Otomato develops a service that tracks users’ on-chain positions and sends alerts when risks, costs or trading opportunities change. The product is aimed at decentralised finance users who manage assets across multiple protocols and blockchains and want to avoid having to check several dashboards at once.

The funding will support product development, expansion across more chains and market segments, and user growth. Backing from Improbable also includes operational support for go-to-market efforts, technical infrastructure, AI development, finance, human resources, legal, and compliance. Otomato will retain control of its intellectual property and product roadmap.

Founded by Chief Executive Officer and Co-Founder Clement Hecquet and Chief Operating Officer Dylan Breugne, Otomato lets users submit wallet addresses so the system can identify positions across lending markets, tokens, NFTs and prediction markets. It currently monitors activity on Ethereum, Arbitrum, Base and HyperEVM.

Since launching as a Telegram bot, Otomato says it has grown without paid marketing. It reports more than 2,000 users, with over 1,500 actively receiving alerts, and says it integrates with more than 10 protocols, including AAVE, Pendle, Uniswap, Morpho, Euler and Hyperliquid.

The company later released a mobile app on Apple’s App Store and Google Play. It also highlighted a campaign tied to the HyperEVM ecosystem that, it said, attracted 3,690 unique users in five days, converted 81.6% of them into Telegram bot users and was shared 971 times on X without paid promotion.

Growth focus

The investment offers clearer insight into how Improbable is positioning itself following the launch of the Somnia Layer 1 blockchain, built on technology developed by the group. Improbable says Somnia has since reached a peak valuation of USD $1.9 billion and that it has delivered total exits of more than USD $179 million to date.

The Otomato deal reflects a venture-building model rather than a passive investment approach. Improbable describes itself as working alongside founders while leaving long-term control with the companies it backs.

That approach was central to its rationale for the investment.

“The opportunity Otomato is pursuing is enormous. DeFi is becoming the back-end of a larger AI-powered economy, and the first team to build the intelligence layer that understands what users actually hold and tells them what matters has the potential to win an entire category. Otomato gives customers agency and full control of their positions, something that they were lacking before. What convinced us to back Clement, Dylan, and the team was their drive. They shipped a product users genuinely love, grew it virally with no paid spend, and they are moving faster than almost any team I have seen at this stage. That is exactly the kind of founder we set out to build with,” said Herman Narula, Co-Founder and CEO of Improbable.

Market challenge

Active users of decentralised finance products often hold positions across several blockchain networks and applications, creating a fragmented view of risk. Services that promise alerts have emerged to address that problem. Still, Otomato argues that broad notifications can create too much noise and fail to reflect what an individual user actually owns.

Its product is positioned as a more personalised model, focused on portfolio-level monitoring rather than general market warnings. That places it in a part of the crypto software market that seeks to make increasingly complex trading and lending activities easier for retail and more active users to follow.

For Improbable, the investment extends a record of building businesses around emerging technology themes beyond its earlier work in gaming, virtual worlds and defence. The UK-based company has spent more than a decade developing and commercialising software businesses and is now concentrating more heavily on ventures linked to AI and web3.

Hecquet said practical support from Improbable had already influenced the company’s direction. 

“We chose Improbable because they are builders that bring more than a passive check. From day one we have had hands-on support on go-to-market, product, and scaling decisions from an executive team that has done this before. That is what moves a company like ours from 2,000 users to millions, and it is what made the decision easy. We look forward to building together as we scale Otomato,” said Clement Hecquet, CEO and Co-Founder of Otomato.



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