Emma AI Achieves ISO 27001 Compliance

Product

ISO 27001

Industry

Health Tech / Artificial Intelligence

Company size

2 – 10 employees

Location

Southend-on-Sea, UK

Partner

Tempo Audits

emma-iso27001-certification-axipro

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Introduction

In health and social care, trust isn’t optional; it’s everything. Sensitive data flows through every interaction, from patient assessments to daily visit notes. That’s why Emma AI, a fast-growing health tech company based in the UK, decided to pursue ISO 27001 compliance.

With its AI-driven tools transforming how organizations deliver person-centered care, Emma AI needed to ensure that data protection and security governance matched its innovation. Their mission was clear: keep care personal while keeping data protected.

To make it happen, Emma AI partnered with Axipro as its advisory partner, leveraging Drata for automation and Tempo Audits as the independent audit partner. Together, they set out to achieve ISO 27001 certification within just six weeks, a goal that demanded focus, coordination, and expert guidance.

About Emma AI

Emma AI is reshaping how social care providers manage quality, compliance, and efficiency. The platform uses artificial intelligence to help organizations audit faster, document smarter, and deliver better care.

From automating service user reviews to streamlining quality assurance, Emma AI’s technology gives care managers back their most valuable resource, time. Tasks that once took hours now take minutes, freeing professionals to focus on people, not paperwork.

But as Emma AI’s reach expanded, so did its responsibility. Handling sensitive health and social care data meant that ISO 27001 compliance wasn’t just a regulatory box; it was a promise to every partner, caregiver, and service user that data would always remain secure.

Challenge: Scaling Compliance

For Emma AI, the road to ISO 27001 compliance came with familiar challenges that many scaling tech companies face:

  • Tight timelines — six weeks to reach audit readiness.
  • High stakes — working in health and social care meant managing regulated, sensitive information.
  • Growing data infrastructure — ensuring their AI systems met security standards without disrupting ongoing innovation.

Their goal wasn’t just certification. It was to build a stronger foundation of trust, one that matched their mission to bring frontier technology into care with safety, precision, and confidence.

Solution: Axipro’s Guided Transition

Emma AI didn’t just want a certificate; they wanted a system that would strengthen how they worked every day. That’s where Axipro stepped in as their advisory partner.

Together, we mapped out a clear and realistic roadmap for ISO 27001 compliance. The Emma AI team took charge of their internal processes while we provided guidance, templates, and hands-on coaching to help them align with the ISO 27001 framework.

Through Drata, the team automated evidence collection, monitored security controls, and tracked progress in real time. This reduced the stress of manual tracking and made every step transparent and measurable.

With Tempo Audits as the independent audit partner, Emma AI moved confidently through the certification process, backed by expert advice, automation, and a commitment to continuous improvement.

One of Emma AI’s leaders, George Parry, described it best:

Axipro was instrumental in helping us achieve ISO 27001 certification. From start to finish, they were proactive, hands-on, and always on top of the details. They made it crystal clear what evidence was required, so all we had to do was gather and submit it. Their structured approach meant we completed everything within the six-week timeframe they set. I’d highly recommend Axipro to any organisation looking to streamline and accelerate their compliance journey.

Results: Compliance Achieved

In just six weeks, Emma AI reached a major milestone: full ISO 27001 compliance.

Here’s what that meant for the company:

  • A recognized ISO 27001 certification, confirming their commitment to data security and governance.
  • Greater trust and credibility with partners and health organizations.
  • Improved visibility and control over security risks, powered by Drata automation.
  • A stronger compliance culture within the team, and every employee now plays an active role in maintaining information security.

For Emma AI, achieving ISO 27001 compliance wasn’t the end of the journey; it was the beginning of a stronger, more accountable future.

Why Emma AI Chose Axipro

Emma AI chose Axipro because they wanted a partner who could move fast, communicate clearly, and make the path to ISO 27001 compliance easier to navigate.

Three reasons stood out:

  1. Trusted Drata Partnership – As one of the most reviewed Drata partners, Axipro understood exactly how to integrate automation into Emma AI’s compliance strategy.
  2. Quick Responsiveness – With a six-week timeline, fast and clear communication made all the difference.
  3. Referrals and Reputation – Like many of our clients, Emma AI was referred through Drata’s partner network, knowing they’d receive expert advisory support backed by proven results.

With advisory guidance from Axipro, automation through Drata, and certification by Tempo Audits, Emma AI achieved ISO 27001 compliance with precision and purpose, without losing focus on what matters most: delivering high-quality, person-centered care.

Ready to Start Your Compliance Journey?

For Emma AI, achieving ISO 27001 compliance wasn’t just a checkbox; it was a milestone that proved their commitment to safeguarding sensitive data in health and social care. It built confidence with partners, boosted credibility with clients, and set a higher standard for data protection across their entire organization.

Your business can do the same. Whether you’re innovating in AI, health tech, or SaaS, the path to ISO 27001 compliance starts with the right guidance and a clear plan.

At Axipro, we help fast-growing companies simplify compliance without slowing down innovation. Through structured milestones, automation with Drata, and trusted audit partnerships like Tempo Audits, we make ISO certification practical, transparent, and achievable.

To take the first step? Book a free consultation with Axipro today and see how easy it can be to turn compliance into your next competitive advantage.

SOC 2 BCDR Tabletop Exercise: A 3-Week Playbook for First-Time GRC Leads

A SOC 2 auditor will not accept a business continuity plan that has never been tested. The AICPA Trust Services Criteria require you to test your recovery procedures, and a written plan sitting in a shared drive does not count. A BCDR tabletop exercise, a facilitated discussion where your team walks through a simulated disaster and makes the decisions a real incident would demand, is the most practical way for a lean team to produce that evidence. This playbook takes a first-time GRC lead from zero to a completed, documented, audit-ready tabletop exercise in three weeks. It covers which Trust Services Criteria the exercise maps to, how to design a realistic scenario, how to run the session, and exactly which artifacts to hand your SOC 2 auditor. No prior exercise experience is assumed, and no external facilitator is required, though we will be honest about when hiring one makes sense. The stakes are real. An untested disaster recovery plan is one of the most common sources of exceptions in SOC 2 reports that include the Availability category. The fix costs one afternoon of your team’s time plus the preparation around it. Few controls offer a better ratio of audit value to effort. Understanding SOC 2 Requirements for BCDR Tabletop Exercises Relevant Trust Services Criteria (A1.2, A1.3, CC7.5) SOC 2 never uses the phrase “tabletop exercise.” What the AICPA Trust Services Criteria require is outcomes, and three criteria do most of the work here. A1.2 requires the entity to design, implement, operate, and monitor environmental protections, data backup processes, and recovery infrastructure. This is where your Business Continuity Plan (BCP) and Disaster Recovery Plan (DRP) live. A1.3 is the one that forces the exercise: the entity tests recovery plan procedures supporting system recovery to meet its objectives. A plan with no test behind it draws an exception under A1.3. CC7.5 sits in the Common Criteria, which apply to every SOC 2 report regardless of category selection, and covers the activities an entity implements to recover from identified security incidents. Its points of focus contemplate periodic testing of incident recovery plans, which means even a Security-only report benefits from a documented exercise. CC9.1, on mitigating the risk of business disruption, reinforces the same expectation. One nuance worth knowing: A1.2 and A1.3 A1.2 and A1.3 only apply if the Availability category is in your report scope. Most SaaS companies include it because customers buying uptime SLAs expect it. If Availability is in scope and your recovery plan has never fired, you have a finding waiting to happen. Auditor Expectations for Tabletop Evidence Auditors test whether the control operated, not whether a meeting happened. For a tabletop, that means they want a dated record showing a defined scenario, named participants, the decisions made, the gaps identified, and proof those gaps were remediated. An exercise that produced zero findings reads as theater, and experienced auditors treat it with suspicion. The strongest evidence package traces a straight line from exercise to finding to closed remediation ticket. Frequency matters too. The Trust Services Criteria set no fixed interval, but annual testing is the floor auditors accept in practice, and your own policy language usually commits you to it. For a Type II report, the exercise must fall inside the observation period. An exercise run fourteen months before your audit window opened does not cover the period under examination. Common Pitfalls First-Time GRC Leads Face​ The same mistakes repeat across first audits. Teams run the exercise but never write the after-action report, leaving nothing to sample. They test a scenario their control language does not describe, such as discussing a ransomware response when their documented control promises an annual backup restoration test. They invite only the engineering team and skip the executives who would make the actual call on customer communication and legal notification. And they treat the exercise as a pass/fail event rather than a gap-finding tool, which produces defensive participants and an empty findings log. Insider Note: In our audit readiness work, the single most common tabletop failure is a mismatch between the control description and the test performed. If your System Description says you “test backup restoration annually,” a discussion-based tabletop does not satisfy that sentence. Read your own control language before you design the exercise, and either run the technical restore alongside the tabletop or fix the control wording to match what you actually do. Pre-Exercise Prerequisites Required Documentation (BCP, DRP, Incident Response Plan) You cannot test a plan that does not exist. Before week one begins, three documents need to be in a reviewable state: a Business Continuity Plan covering how the business keeps operating through a disruption, a Disaster Recovery Plan covering how systems and data are restored, and an Incident Response Plan (IRP) covering detection, escalation, and communication. They do not need to be perfect. Half the value of a tabletop is discovering where these documents are wrong, and a slightly stale plan produces a richer exercise than a freshly polished one. What matters is that each document names owners, defines Recovery Time Objectives (RTO) and Recovery Point Objectives (RPO) for critical systems, and includes current contact information. If you have no plans at all, stop and write them first. A tabletop without underlying plans is a brainstorm, and auditors will not accept it as recovery plan testing. Identifying Stakeholders and Participants A SOC 2 tabletop needs decision-makers, not just responders. The core group is the engineering or infrastructure lead who owns recovery execution, the CTO or equivalent who owns the restore-versus-rebuild call, someone who owns customer communication, and whoever handles legal or contractual notification obligations. For a startup under 50 people, that is typically five to eight participants. Keep the room small enough that everyone speaks. Finance and HR can join larger exercises later; for a first exercise, they dilute focus. Defining Scope, Objectives, and Success Criteria Write the objectives down before designing anything else, and map each one to a Trust Services Criterion. Three objectives are the right

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MAS AI Risk Management Guidelines Are Final: What AI Vendors Selling to Financial Institutions Must Do Before October 2027

On October 7, 2026, the Monetary Authority of Singapore issued its final Guidelines on AI Risk Management, and the clock is now running. Every financial institution in Singapore has until October 7, 2027 to meet the core supervisory expectations, with the remaining sections due by October 7, 2028. The Guidelines apply to all FIs and all forms of AI, from a chatbot embedded in a support tool to autonomous agentic systems. Here’s the part most coverage will miss: the most commercially significant clause is not aimed at banks at all. MAS makes financial institutions fully accountable for third-party AI, including AI developed, operated, or provided by vendors. FIs must obtain sufficient assurance from those providers, and if they cannot, MAS expects them to limit, suspend, or replace the service. If you sell AI-powered software to banks, insurers, payment firms, or asset managers with a Singapore presence, that sentence is about you. Over the next twelve months, your FI customers will start asking how your AI is governed, and a security questionnaire alone won’t answer the question. This article covers what the Guidelines require, why vendors are effectively in scope, and how ISO 42001, the international standard for AI management systems, maps onto MAS expectations. What the MAS AI Risk Management Guidelines Require The Guidelines apply to all financial institutions in Singapore and all forms of AI technology, including generative AI and systems with growing autonomy in decision making or execution. MAS kept the framework principles-based and risk-proportionate: each FI decides how to meet the expectations based on the nature and scale of its AI use and the materiality of the risks involved. The final text follows a public consultation in November 2025, and MAS retained the core expectations while refining them in response to industry feedback. The expectations fall into four areas. Oversight with clear accountabilities Boards and senior management must provide effective oversight of AI risks, with defined roles and responsibilities, a stated risk appetite, and risk management frameworks, policies, and procedures. One pragmatic concession from the consultation: FIs can use existing governance structures where they provide adequate oversight, and no dedicated AI committee is required. Risk management across the AI life cycle FIs must identify where AI is used, maintain AI inventories at an appropriate level of granularity, assess the risk materiality of each use case, and apply proportionate controls across the life cycle: data governance, testing, human oversight, cybersecurity, monitoring, and change management. These controls must be reviewed as AI use expands, with MAS specifically calling out the rise of agentic AI systems that operate autonomously and access tools. Accountability for third-party AI FIs remain fully accountable for AI used in their services, including AI developed, operated, or provided by third parties. More on this below, because it’s the clause that reaches beyond the regulated sector. Proportionate application FIs whose AI use is unlikely to have a material impact on themselves, their customers, or other stakeholders can meet the expectations with basic policies and procedures. The sophistication of controls should scale with risk exposure. That’s sensible regulation, but it doesn’t exempt anyone. Even an FI running only embedded AI in vendor tools still has to show it assessed that conclusion. The Third-Party AI Clause: Why Vendors Are Now in Scope MAS doesn’t regulate software vendors. It doesn’t need to. By making FIs accountable for the AI inside the services they buy, the Guidelines turn every regulated institution into an enforcement mechanism pointed at its supply chain. The release spells out the sequence. FIs must obtain sufficient assurance from third-party AI providers, assess whether the third-party AI is suitable for its intended use, and apply compensating controls where assurance gaps exist. If the risks still cannot be brought within the FI’s risk appetite, MAS expects the institution to limit, suspend, or replace the third-party AI service. That last option is the commercial teeth: a vendor who can’t show it governs its AI is now, by supervisory expectation, a vendor the FI should consider dropping. This is the same transmission mechanism that made SOC 2 the default ask for Singapore companies selling software to US and global enterprises. The regulator never names the vendor, but the buyer’s obligation becomes the vendor’s sales blocker. Anyone who has watched a deal stall on a security questionnaire knows how this plays out: the questionnaire grows an AI governance section, the generic answers stop being accepted, and procurement asks for third-party evidence. Insider Note: The scope is wider than most vendors assume. MAS defines AI to include embedded AI inside tools an FI already uses, not just standalone models. A CRM with an AI scoring feature, a fraud tool with a model under the hood, or a support platform with a generative assistant all land in the FI’s inventory, which means their vendors all land in the assurance process. For fintechs, regtechs, and AI-native SaaS companies selling into Singapore’s financial sector, the practical question is no longer whether FI customers will ask about AI governance. It’s what evidence you hand them when they do, and whether it survives the FI’s own supervisory scrutiny. The Timeline: What Happens Between Now and October 2028 MAS gave the sector a phased runway, and each date on it changes vendor behavior before it changes FI behavior. Date What happens October 7, 2026 Final Guidelines issued by MAS 2027 MAS consults the sector on additional guidance for agentic AI October 7, 2027 Core expectations take effect (Sections 3 to 4: oversight and AI risk management) October 7, 2028 Remaining expectations take effect (Sections 5 and 6) The twelve months before October 2027 are when FIs build their AI inventories and run their first third-party assurance exercises. Vendors will feel the Guidelines during this window, well before the formal effective date, because an FI can’t certify its own readiness without first interrogating its suppliers. Compliance deadlines propagate backward through the supply chain. Two signals say this won’t stay a Singapore story. First, the Financial Stability Board has consulted

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AI-Generated Code vs SOC 2 and ISO 27001 Change Management

Gartner predicts that by 2028, 90% of enterprise software engineers will use AI code assistants, up from less than 14% in early 2024. SOC 2 and ISO 27001 change management controls were written before that shift, and both rest on an assumption that AI-generated code breaks outright: the person who approved a change wrote it, or at least fully understood it. Neither the AICPA nor ISO has published AI-specific change management requirements, so auditors apply the existing controls, SOC 2 CC8.1 and ISO 27001 Annex A 8.32, to commits no human authored. Most teams discover the mismatch mid-audit, when a sample pulls up a 2,000-line agent-generated pull request that was approved in four minutes. This guide maps AI code generation to both frameworks: what each one requires, the risks AI coding assistants introduce, the workflow that satisfies auditors, and the evidence they request when GitHub Copilot, Cursor, or Claude Code shows up in your SDLC. Why AI-Generated Code Breaks Traditional Change Management Change management controls assume a human bottleneck. AI removes it in three places at once. The Volume Problem: AI Commits at Machine Speed A single developer running an agentic coding tool can open more pull requests in a day than a small team used to produce in a sprint. Controls designed around weekly release trains and change tickets now face dozens of daily changes, and every one of them is technically in scope for CC8.1 and A.8.32. The control does not scale by adding reviewers. It scales by automating the parts of change management that never needed human judgment, and reserving humans for the parts that do. The Attribution Problem: Who “Authored” the Change? Git records the committer, not the author in any meaningful sense. When an engineer prompts an assistant and accepts a 400-line diff, the commit metadata says a human wrote it. When an autonomous agent opens a pull request from a service account, the metadata says a bot wrote it, with no record of which model, which version, or which prompt produced the change. Code provenance, the ability to trace a change back to its true origin, is shifting from an advanced practice to a baseline audit expectation, and most repositories cannot answer the question today. The Review Problem: Human Approval in an AI-Driven Pipeline Approval only means something if the approver engaged with the change. AI output arrives faster than humans can read it, and review depth degrades accordingly. The control on paper says reviewed and approved. The control in operation is often a reflexive click on a diff nobody read end to end. Insider Note: Approval timestamps give this away immediately. When an auditor samples pull requests and finds a 1,500-line AI-generated diff approved three minutes after it was opened, with no reviewer comments and no requested changes, they treat the review control as not operating, regardless of what the policy document says. What SOC 2 CC8.1 Requires for Change Management CC8.1 is the change management criterion in the AICPA’s Trust Services Criteria. The entity authorizes, designs, develops or acquires, configures, documents, tests, approves, and implements changes to infrastructure, data, software, and procedures. Auditors translate that language into four practical expectations, and none of them carves out an exception for AI. Documented Change Request and Authorization Every production change traces back to an authorized request: a ticket, an issue, or an approved pull request tied to a business reason. A prompt typed into a coding assistant is not a change request. The output of that prompt still needs to land inside your normal authorization flow. Segregation of Duties Between Developer and Approver The party that writes a change cannot be the only party that approves it. Translated to AI-assisted development: the model that generated the code cannot also be the control that approves it, and the engineer who prompted the agent counts as the developer for segregation purposes. Testing and Rollback Procedures Auditors expect evidence that changes were tested before deployment and can be reverted when they fail. AI complicates this in one specific way: when the same model writes both the implementation and its tests, the tests reflect the model’s reading of the requirement, not an independent verification of it. A human still needs to own test coverage intent. Evidence of Operating Effectiveness Over Time A SOC 2 Type II report tests whether controls operated across the entire review period, typically 3 to 12 months. One quarter of enforced branch protection does not cover a 12-month window. If you adopted agentic coding mid-period without updating controls, expect the auditor to test both regimes and flag the uncontrolled months. That timing problem is why this work belongs before the audit window opens, not during it. What ISO 27001 Requires for Change Management ISO/IEC 27001:2022 handles change management through three Annex A controls that interlock, plus clause 6.3 of the management system itself, which governs planned changes to the ISMS. Adopting an AI coding tool touches all of them. Annex A 8.32 (Change Management) Explained A.8.32 requires that changes to information processing facilities and information systems follow change management procedures. The accompanying ISO 27002 guidance expects changes to be planned, risk-assessed, tested, approved by an authorized party, logged, and reversible through a documented fallback. Two things matter for AI-assisted teams. First, the control covers the development pipeline itself, so the configuration of your AI tooling, agent permissions, and CI/CD gates is in scope, not just application code. Second, certification bodies audit against the 2022 revision, so documentation written for the 2013 version will not hold up. Annex A 8.28 (Secure Coding) and AI-Assisted Development A.8.28 is new in the 2022 revision and requires secure coding principles applied before, during, and after development, including for externally sourced components. AI-generated code is, functionally, externally sourced code of unknown provenance: trained on public repositories, reproduced probabilistically, and capable of importing insecure patterns or unvetted dependencies. Your secure coding standard needs to say explicitly how AI output is vetted, which usually means mandatory static analysis, software composition

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