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OpenAI Taps Telon to Bring Advanced AI Workflows to Lawyers and Legal Teams

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OpenAI’s latest move into the legal sector signals a shift in how enterprise AI may be deployed: rather than simply giving lawyers access to powerful models, the company is building an ecosystem around implementation, training and workflow integration.

Its newly announced partnership with London-based startup Telon illustrates that strategy. Telon, founded in June 2026 by former trial attorney Lewis Bretts, specializes in what it calls “legal engineers.”

These professionals are primarily former lawyers who help law firms and corporate legal departments configure AI systems, develop prompts and agents, and train employees to use the technology effectively.

OpenAI has designated Telon a “select partner,” bringing the startup into its broader Partner Network. The significance of the arrangement lies in the problem it attempts to solve. Artificial intelligence has already demonstrated its usefulness for legal research, document analysis, drafting and other knowledge-intensive tasks.

Yet purchasing an AI subscription does not automatically transform a law firm’s workflow. Lawyers still need to understand how to structure requests, evaluate outputs, integrate AI into existing systems and maintain appropriate human oversight.

That implementation gap is becoming an important battleground in enterprise AI. OpenAI’s own work with law firms illustrates the opportunity.

Australian firm Gilbert + Tobin has reported using ChatGPT and Codex across its operations, reducing some recruitment research and data-extraction work from roughly four hours to 20 minutes and cutting selected conflict, KYC and AML checks to about five minutes.

The firm says governance and human accountability remain central to its deployment. OpenAI has also collaborated with Willkie Farr & Gallagher on firmwide AI adoption and the development of proprietary AI platforms through the firm’s innovation organization.

These examples suggest that the legal market is moving beyond experimentation toward deeper integration of generative AI into professional workflows. Telon’s model adds another layer. Instead of building another standalone legal AI application.

It positions people with legal expertise between the technology and the organizations using it. That approach could prove important because legal work carries unusually high requirements for accuracy, confidentiality and professional judgment.

A lawyer cannot simply accept an AI-generated answer because it sounds convincing. Legal conclusions must be supported by authoritative sources, relevant facts and applicable law. Errors can create financial, regulatory and reputational consequences.

The human lawyer therefore remains responsible for judgment even when AI performs much of the preliminary work.

This makes the “legal engineer” potentially more than a technical consultant. The role sits at the intersection of legal practice, software engineering and AI workflow design.

The partnership represents a way to scale specialized expertise without building every industry-specific implementation capability internally. The company launched its Partner Network in June, with a broader goal of enabling consultants and partners to deploy its technology inside organizations.

The legal industry is already crowded with specialized AI providers, including companies such as Harvey and other legal technology firms. Reuters reported earlier this year that AI companies were increasingly competing for law firms and even law students as the next generation of legal professionals becomes an important customer base.

The emerging competition is therefore not simply about whose model is most capable. It is increasingly about who can make AI reliable, usable and deeply embedded in professional environments. OpenAI’s partnership with Telon points toward that next phase.

The future of AI in law may depend less on replacing lawyers than on redesigning how lawyers work—with AI handling increasingly complex tasks while human professionals retain responsibility for interpretation, strategy and accountability.

Why Cloud Security Is Essential for Business Digital Transformation

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A mid-sized firm can move customer portals, analytics workloads, and development environments to the cloud within months. But its security operating model rarely changes at the same speed, and this gap is where digital transformation begins to carry more risk than the board expected.

That’s why cloud security matters because transformation expands the number of identities, applications, data stores, and connections that security teams must track. Protecting that expansion without turning every release, acquisition, or migration into a fresh security project requires a clear operating model.

Cloud Adoption Changes the Shape of Business Risk

Cloud migration isn’t merely a change in hosting location. It changes who can access business systems, how applications communicate, and which teams can build infrastructure. Therefore, understanding cloud security for digital businesses gives technology and security leaders a useful starting point for examining these changes.

But it’s also messy. A developer may deploy a storage service for a short-lived project, but a business unit connects a new software platform without involving the SOC. Neither action looks alarming on its own; however, six months later, the organization has sensitive data spread across several services, inconsistent access controls, and no agreed owner for half the alerts.

Shared Responsibility Still Leaves Work for the Customer

Cloud providers protect their underlying infrastructure, but customers remain responsible for identities, data, configurations, and application behavior. The exact boundary changes between infrastructure, platform, and software services.

The UK National Cyber Security Centre’s shared responsibility guidance recommends understanding which security duties remain with the customer for each service and deployment model.

This may sound obvious, but incident reviews suggest otherwise. Teams often discover too late that nobody was monitoring privileged access, checking public exposure or testing whether backups could be restored outside the affected account.

Why Digital Transformation Depends on Cloud Security

Transformation programs are judged by business movement: shorter release cycles, faster market entry, lower operating friction and better use of data. Security needs to support those outcomes, not sit beside them as a separate technical function.

It Keeps Rapid Change from Creating Hidden Exposure

Cloud environments can change hundreds of times in a day. New workloads appear, permissions are edited, and services connect through APIs. A quarterly configuration review can’t keep pace with that rate of movement.

That’s why security teams need continuous visibility into assets, identities, data flows and configuration changes. The hard part isn’t collecting more alerts; it’s finding the few changes that create a credible route to sensitive data or critical operations.

Context matters because an exposed test system with synthetic data doesn’t carry the same risk as an exposed administrative interface connected to a production database. Treating both as equal wastes time.

It Protects the Identities Behind Cloud Activity

In traditional networks, defenders could place considerable trust in location. But cloud services have weakened that assumption because employees, contractors, applications, and automated workloads may connect from almost anywhere.

That’s why in this system, identity becomes the practical control point. So, businesses and cloud security operators should start with phishing-resistant authentication for privileged roles, short-lived credentials for workloads, and tightly scoped permissions.

After that, focus on removing dormant accounts, while emergency access accounts need separate monitoring and regular tests. Service identities also deserve the same scrutiny as human administrators, sometimes more, since they’re easily forgotten and may hold broad permissions.

It Makes Compliance Evidence Easier to Produce

Regulators and customers don’t accept “the provider handles security” as sufficient evidence. Organizations need to show where regulated data is stored, who can access it, how long logs are retained, and what happens when a supplier or region becomes unavailable.

The European Union Agency for Cybersecurity offers a cloud security risk and procurement guide that links cloud risks with questions organizations can ask providers. Although written for smaller businesses, the procurement logic scales well: get evidence, clarify contractual duties, and examine exit conditions before moving important workloads.

Good cloud security also creates an auditable trail as part of normal operations. That’s far better than assembling screenshots and spreadsheets a week before an assessment.

It Gives Incident Responders Something They Can Actually Use

Cloud incidents are different from conventional endpoint compromises. An attacker may misuse a valid token, change a policy, create a new access key, and copy data through an approved service, and that’s why standard malware alerts never appear.

So the question is: Can the SOC reconstruct that activity quickly? If the answer is “probably”, the organization isn’t ready.

Useful preparation includes centralized control-plane logs, synchronized timestamps, protected log storage, and tested procedures for revoking sessions. Responders also need access to cloud administrators who understand the affected environment.

A Practical Cloud Security Framework for Transformation Teams

Security works best when it’s built into the transformation plan before workloads move. Here, the following five questions can expose most early weaknesses:

  1. What are we moving? Record the data, applications, dependencies, and business owners.
  2. Who can access it? Include administrators, developers, suppliers, service accounts, and automated pipelines.
  3. Which controls are native to the service? Use managed security capabilities where they meet the requirement rather than rebuilding familiar data-center patterns.
  4. What must the SOC be able to see? Define mandatory logs, retention periods, alert routes, and escalation owners.
  5. How will we recover or leave? Test restoration, account isolation, and data portability before a crisis forces the question.

Measure Exposure, Not Security Activity

Counting blocked events or generated alerts says little about business risk. Better cloud security measures include the number of privileged identities without strong authentication, internet-facing assets with known weaknesses, unencrypted sensitive stores, and critical workloads without a tested recovery path.

Time also matters, which is why you should track how long high-risk misconfigurations remain open and how quickly you can revoke access across cloud services during an incident.

Cloud Security Lets Transformation Move Without Losing Control

Digital transformation creates value by allowing organizations to change faster. Yet speed becomes expensive when nobody can explain where sensitive data sits, which identities control it, or how the business would recover after an account takeover.

Cloud security provides that missing discipline. It connects architecture, identity, monitoring, compliance, and recovery to the transformation program itself, instead of adding controls after deployment. The goal isn’t to eliminate every technical risk- that won’t happen but to keep them visible, owned, and within the organization’s tolerance while the business continues to build.

How a Security Operation Centre Strengthens Digital Business Resilience

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A retailer’s payment platform begins generating unusual authentication traffic late on Friday. Nothing has failed yet, customers are still checking out, and the infrastructure team sees no obvious outage.

But somewhere between those signals and the next board update, the business may be heading towards fraud, data loss, or days of disrupted trading.

A Security Operation Centre gives the enterprise a coordinated way to detect that shift, judge its business significance, and contain the damage before a technical event becomes an operational crisis. Its value isn’t confined to monitoring.

Done well, it connects security decisions with service continuity, regulatory duties, customer trust, and recovery priorities.

Resilience Depends on Decisions, Not Dashboards

Security teams rarely suffer from a total absence of data. The usual problem is fragmentation. Network telemetry sits in one system, endpoint alerts in another, cloud activity somewhere else, while identity events arrive without enough context to show whether an account belongs to an intern or the finance director.

A modern Security Operation Centre framework brings those signals into an operating model built around monitoring, investigation, response, recovery, and continued refinement.

The technology matters, of course. Yet the real test is whether analysts can turn scattered evidence into a sound decision while the clock is running.

It Connects Technical Activity to Business Impact

An alert marked “critical” by a security product isn’t automatically the organisation’s most urgent problem. A medium-severity identity anomaly affecting a privileged administrator may carry far greater business risk than malware found on an isolated test machine.

Mature teams add context before assigning priority:

  • Which business service is involved?
  • Does the affected identity have privileged access?
  • Is regulated or commercially sensitive data exposed?
  • Could containment interrupt revenue or customer access?
  • Who owns the recovery decision?
  • Is legal, regulatory, or executive notification required?

This changes the discussion. Analysts stop treating every signal as an isolated technical object and start asking what failure would mean for the business.

Faster Detection Shrinks the Damage Window

Most incidents don’t begin with a cinematic system failure. They start quietly: an unusual login, a newly created account, a suspicious process, or a small outbound data transfer that looks harmless without supporting evidence.

The Security Operation Centre looks for relationships between those events. A questionable login becomes more serious when it’s followed by mailbox rule changes, privilege escalation, and access to a sensitive cloud repository. Correlation gives the team a story, even if the first chapter looked routine.

Speed still isn’t the only measure. A hurried but poorly judged response can cut off a customer platform, erase useful forensic evidence, or alert an intruder before the team understands the intrusion path.

Good Triage Protects Analyst Attention

What should happen when thousands of alerts arrive each day?

Not every alert deserves a human investigation. Known false positives should be tuned out, repetitive low-risk actions can be automated, and cases with credible business impact should move quickly to experienced analysts. That sounds obvious. In practice, tuning often slips behind project work until noise becomes normal.

A workable triage model should consider confidence, asset value, user privilege, exposure, and potential blast radius. Analysts also need permission to challenge the default severity assigned by a tool. Context beats colour coding.

Response Must Be Designed Before the Incident

Incident response isn’t a document that sits on a shared drive until audit season. It’s a set of decisions teams have already discussed, tested, and refined before pressure arrives.

Can the SOC isolate an executive laptop without waiting for multiple approvals? Who has the authority to temporarily suspend a revenue-generating application? If ransomware spreads across a shared environment, which services come back first? Questions like these reveal the gap between having a response plan and being genuinely prepared to execute it.

Guidance from the Centre for Internet Security (CIS) emphasizes that effective incident response depends on preparation, clearly defined roles, asset visibility, communication workflows, and coordinated recovery activities rather than technology alone.

That reflects how real incidents unfold. Detection is only one piece of the puzzle. Preparation, governance, business alignment, stakeholder communication, and recovery planning often determine whether an incident becomes a short disruption or a prolonged operational crisis.

Use Playbooks, but Leave Room for Judgement

Playbooks reduce hesitation during familiar events such as credential theft, malware detection, cloud account compromise, or suspected data exfiltration. They should define evidence to collect, containment options, escalation paths, communication triggers, and recovery checks.

Still, no playbook survives first contact unchanged.

Consider a mid-size financial services firm migrating workloads to a hybrid environment. Isolating a compromised server may be technically simple, yet doing so could interrupt payment processing or remove evidence needed for regulatory review. The analyst needs a decision path, not just an automated button.

Tabletop exercises help uncover those conflicts before a real incident. They also reveal outdated contact lists, unclear ownership, unavailable backups, and dependencies nobody documented.

Recovery Is Where Resilience Becomes Visible

Containment stops immediate harm. Recovery decides whether the organisation can return to normal without reopening the same weakness.

The SOC should work with infrastructure, application, identity, legal, communications, and business teams to validate recovery. That means checking restored systems, rotating exposed credentials, monitoring for renewed activity, and confirming that critical services behave as expected.

The work continues after systems come back.

A useful post-incident review asks what delayed detection, which controls failed, where teams lost time, and whether the original business impact estimate was accurate. Findings should lead to owned actions with dates. Otherwise, lessons learned become minutes from a meeting nobody revisits.

For regulated organisations, this operational discipline also supports accountability. Tekedia’s overview of cybersecurity guidelines for Nigerian financial institutions reflects the close relationship between cyber risk management, operational stability, and public confidence.

Measure Outcomes the Board Can Use

Alert totals make busy dashboards, but they say little about resilience. Leaders need measures tied to exposure and interruption.

Useful indicators include:

  • Time taken to validate a credible incident
  • Time from validation to containment
  • Percentage of critical assets with usable telemetry
  • Repeat incidents caused by unresolved root issues
  • Recovery time for priority business services
  • Playbook performance during exercises and live cases
  • High-risk access or asset gaps awaiting remediation

These figures won’t answer every board question. They do, however, show whether the organisation is getting quicker, sharper, and less likely to repeat an expensive mistake.

Digital Resilience Is an Operating Habit

A Security Operation Centre strengthens digital business resilience when it helps the organisation make better decisions under pressure. That requires visibility, skilled investigation, rehearsed authority, credible recovery plans, and honest review after the event.

The strongest SOC isn’t necessarily the one with the most screens or the largest alert queue. It’s the one that can recognise a developing problem, explain what the business stands to lose, act without confusion, and help critical operations return safely. Cyber incidents will still happen. Whether they become prolonged business failures is a different question.

UK Workers Spend Nearly £1 Billion a Year on AI Tools for Their Jobs – Deloitte

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One in six workers in Britain is paying out of their own pocket for an artificial intelligence tool to help with their job, collectively spending nearly £1 billion ($1.4 billion) a year, according to research published by Deloitte on Wednesday.

The findings point to a widening gap between how quickly employees are adopting generative AI and how quickly employers are making the technology available in the workplace. Rather than waiting for companies to approve or provide AI tools, a significant share of workers are turning to services such as ChatGPT, Claude, Google Gemini and Microsoft Copilot themselves.

Deloitte’s inaugural GenAI Workforce Survey found that about two-thirds of workers had used generative AI tools, while nearly a quarter were using them every day as part of their jobs. More than three in 10 respondents, or 31%, said they were using AI without their employers’ knowledge, a practice commonly referred to as “shadow AI.”

At the same time, 17% said they were personally paying for at least one generative AI tool for work.

“UK workers are showing they don’t want to wait for permission to use GenAI,” said Hayley McKelvey, Deloitte UK’s chief AI officer.

“Many are already using free tools or pay for premium versions themselves to help them get work done.”

The scale of employee-funded adoption suggests that generative AI is no longer confined to formal corporate technology programmes. Workers are increasingly treating AI assistants as ordinary productivity tools, even where their employers have not established an approved platform, policy or budget for their use.

The development has yielded a new set of questions for companies around data protection, intellectual property, security and accountability. When employees independently select AI services, organizations may have less visibility over what information is being entered into those systems and how the tools are being used.

The survey found that the most common workplace applications remain relatively straightforward. Workers primarily use AI to search for information, draft emails, and produce summaries. Those activities nevertheless generated measurable time savings. Respondents estimated that generative AI saved them an average of 70 minutes a week.

Deloitte partner Paul Lee said generative AI had rapidly become part of everyday working life, but its use remained concentrated on relatively basic tasks.

“Yet for many workers its role remains relatively basic, focused on searching for information and drafting content rather than supporting more complex work,” Lee said.

Companies have been assessing whether their growing AI investments are producing meaningful productivity gains. Widespread access to a chatbot can make individual tasks faster, but the larger economic opportunity may depend on whether businesses redesign workflows around AI rather than simply give employees another software application.

“The organizations that gain the greatest advantage from AI will be those that move beyond simply providing access to tools and focus on helping people use them with the appropriate guardrails and purpose,” Lee said.

The survey therefore captures two parallel developments. Employees are already integrating AI into their daily routines, while many organizations are still working out how the technology should be deployed, governed and connected to more complex business processes.

The £1 billion annual spending estimate also reveals an unusual feature of the current AI adoption cycle: part of the cost is being absorbed directly by workers.

Paying for a premium AI subscription, for employees, can be a relatively simple way to gain access to more capable models or additional features. However, widespread unofficial use can make it harder to determine which AI tools are being used, what value they generate and what risks they introduce.

The survey was conducted by Ipsos for Deloitte and covered 25,000 workers, giving the findings a broad view of how generative AI is entering Britain’s workplaces.

The data suggest that the next stage of workplace AI adoption may be less about persuading employees to use the technology and more about bringing existing usage into formal company systems. The challenge for employers is to turn what is currently a largely employee-driven experiment into controlled and purposeful use without removing the productivity gains that encouraged workers to adopt the tools in the first place.

Grab to Buy Atome Financial for $1.49 Billion in Consumer Lending Push

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Grab is buying Singapore-based buy-now, pay-later platform Atome Financial for $1.49 billion in cash as the Southeast Asian technology company moves to deepen its consumer lending business and expand the reach of its financial services operations.

Grab will initially acquire a 60% controlling stake in Atome, with plans to purchase the remaining 40% roughly two years after the first transaction closes. The two-stage structure is intended in part to reduce the capital-allocation risk associated with the deal, Grab Chief Financial Officer Peter Oey told CNBC’s “Squawk Box Asia” on Wednesday.

Grab shares closed 3.64% lower on Nasdaq following the announcement, suggesting investors were initially cautious about the scale and structure of the transaction.

The acquisition gives Grab an established consumer-credit platform that operates across sectors where the company has a more limited presence, including travel, beauty and e-commerce. Atome’s network of merchants and consumers could allow Grab to extend its financial services ecosystem beyond the users and transactions already generated through its mobility, delivery and digital platforms.

“All this together actually brings our capability to the next level when it comes to financial services,” Oey said, adding that the transaction would be accretive to the business and had contributed to Grab raising its 2028 outlook.

The deal is expected to close next year. As a result, Atome’s financial contribution is expected to become more significant toward the latter part of that year and into 2028, according to Oey.

Grab expects its financial services segment to generate $500 million in adjusted EBITDA by 2028, making the Atome acquisition an important part of its effort to turn financial services into a larger contributor to the group’s earnings.

Grab Expands Beyond Payments And Mobility

Grab has spent years building financial services around its broader Southeast Asian consumer ecosystem. Its expansion into lending represents a shift from simply facilitating transactions toward capturing more of the financial activity generated by consumers and merchants using its platforms.

Atome gives Grab an existing business in a segment where customer relationships and merchant acceptance are critical. Rather than building a new buy-now, pay-later operation from scratch, Grab will take control of a platform already connected to brands across several consumer categories.

Oey said Atome’s presence in travel, beauty and e-commerce would give Grab access to areas where its existing operations have less reach.

That diversification could yield positive results as Grab seeks to increase the scale and profitability of its financial services business. Consumer lending can generate higher revenue per customer than basic payment services, but it also introduces greater exposure to credit losses, regulatory requirements, and changing consumer borrowing behavior.

The acquisition therefore expands both the opportunity and the risk within Grab’s financial services operations. The company will need to integrate Atome while maintaining underwriting discipline and managing the regulatory requirements associated with consumer credit across multiple Southeast Asian markets.

Grab plans to retain Atome’s existing management team, Oey said. The decision could help preserve operational expertise and reduce disruption during the integration process.

The two-year gap before Grab acquires the remaining 40% will also give the companies time to develop potential synergies before the full transaction is completed. Oey said the period would allow Grab and Atome to work on those opportunities while limiting the amount of capital committed immediately.

The structure effectively gives Grab time to assess how the businesses work together before completing the acquisition. It also provides a longer window for integrating technology, customers and merchant relationships and determining where the combined platforms can generate additional revenue.

Southeast Asia’s Credit Gap Becomes A Growth Opportunity

Grab’s interest in consumer lending comes as financial access remains uneven across Southeast Asia. Oey pointed to access to fair credit as an area where Grab wants to work with regulators, indicating that the company’s expansion will depend not only on demand but also on the regulatory framework governing digital lending.

The company is also examining micro-investing as another potential financial services opportunity in the region. That broadens the potential ambition beyond lending. A combination of payments, credit, investment products and other financial services could allow Grab to develop a more comprehensive financial ecosystem around its large consumer and merchant base.

However, the economics of such an expansion will depend on execution. Lending businesses can grow rapidly when credit is readily available, but profitability can deteriorate when borrowers struggle to repay or when regulators impose tighter requirements.

For Grab, the attraction of Atome is therefore not simply the addition of another financial product. It is the possibility of using an established consumer-credit platform to extend the company’s reach into transactions and customer relationships that sit outside its traditional strengths.

The $1.49 billion initial cash consideration also makes the deal one of Grab’s more significant capital commitments as it seeks to build a larger earnings contribution from financial services. The staged acquisition allows the company to spread that commitment over time while pursuing potential operating synergies.

The immediate share-price decline shows that the market will scrutinize whether those expected benefits justify the cost and risks of the acquisition. Grab will ultimately have to demonstrate that Atome can contribute meaningfully to earnings while maintaining responsible credit standards across its markets.

The company has set a clear financial target: $500 million in adjusted EBITDA from financial services by 2028. With the Atome transaction expected to contribute more materially from late next year, the performance of the acquired business will become an increasingly visible part of Grab’s progress toward that goal. The deal marks another step in Grab’s evolution from a ride-hailing and delivery platform into a broader consumer technology and financial services company.