Ziwo Upgrades to ISO/IEC 27001:2022 with Axipro

Product

SOC 2 Type I & II

Industry

Cloud Communications / Contact Center Software

Company size

51 – 200 employees

Location

Dubai, UAE

Ziwo-ISO-27001-Axipro

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Introduction

Compliance is not a checkbox. It is a journey. For Ziwo, that journey meant moving beyond their ISO 27001:2013 certification and preparing for the future. They sought to modernize their security practices, reduce manual work, and align with updated international standards.

With Axipro providing advisory guidance, Drata powering automation, and Insight Assurance serving as the audit partner, Ziwo successfully transitioned its compliance program to meet ISO 27001:2022 requirements.

The outcome was a smooth certification process, completed without non-conformities, and a stronger foundation of trust with customers across the Middle East and beyond.

About Ziwo

Ziwo is a leading cloud-native call center and customer experience (CX) platform. Headquartered in Dubai, with offices in Riyadh, Cairo, and Casablanca, Ziwo supports businesses across the MENA region and beyond.

Their platform helps organizations deliver secure, intuitive, and multilingual customer interactions. Industries served include:

  • BPO and customer support
  • Retail and e-commerce
  • Finance and real estate
  • Travel and hospitality

As a trusted provider, Ziwo already held ISO 27001:2013 certification. As a trusted provider, Ziwo already held ISO 27001:2013 certification. But to keep pace with growth and evolving expectations, they chose to pursue ISO 27001:2022 compliance with advisory support from Axipro, while leveraging Drata for automation and working with Insight Assurance as their audit partner.

Challenge: Scaling & Upgrading Compliance

Ziwo’s leadership recognized that while ISO 27001:2013 kept them secure, it also came with challenges:

  • Manual compliance tracking created inefficiencies.
  • Evidence collection for audits consumed valuable time.
  • Processes were fragmented across teams.
  • New ISO 27001:2022 requirements required updated alignment.

The goal was clear: transition smoothly to ISO 27001:2022, reduce administrative overhead, and empower their internal team to manage compliance more effectively. This meant choosing the right advisory, automation, and audit partners to guide the process.

Solution: Axipro’s Guided Transition

Through this coordinated effort, Ziwo:

  • Successfully upgraded from ISO 27001:2013 to ISO 27001:2022 certification.
  • Completed the certification with zero major or minor non-conformities, as confirmed by Insight Assurance.
  • Integrated Drata for continuous, automated compliance monitoring.
  • Strengthened their internal team’s ability to manage compliance independently going forward.

This achievement highlights Ziwo’s commitment to security and governance while also showcasing the value of advisory, automation, and audit working together.

“Thanks a ton for the smooth audit, team! It was a combined effort and great teamwork. We look forward to continuing this success in upcoming audits and certifications on behalf of Ziwo & management.” — Ziwo Management

Results: Smooth Audit, Stronger Governance

The partnership delivered results that mattered. With Axipro’s support and Drata’s automation, Ziwo achieved:

  • ISO 27001:2022 certification with zero major or minor non-conformities.
  • Full Drata integration, giving real-time visibility into compliance status.
  • Audit preparation that shifted from manual to automated, saving weeks of effort.
  • An empowered internal team, now confident in managing and scaling compliance.

This wasn’t just an upgrade, it was a transformation. Ziwo replaced administrative burden with operational efficiency. They gained trust, credibility, and a stronger security posture that customers value.

Thanks a ton for the smooth audit, team! It was a combined effort and great teamwork. We look forward to continuing this success in upcoming audits and certifications on behalf of Ziwo & management. — Ziwo Management

Ready to see the same results? Modernize your audits today with ISO 27001:2022 compliance with Axipro.

Why Ziwo Chose Axipro

For Ziwo, selecting Axipro as an advisory partner was driven by the need for clarity and structure during the transition. They valued Axipro’s ability to:

  • Provide guidance on aligning with ISO 27001:2022 requirements.
  • Support the adoption of automation tools like Drata.
  • Collaborate with internal teams rather than replace them.
  • Partner effectively alongside Insight Assurance as the independent auditor.

Axipro’s role was not to certify or audit, but to act as a trusted advisor, helping Ziwo navigate the complexity of compliance and build confidence in their processes.

Conclusion

Compliance is not a one-time milestone. It evolves as standards change and organizations grow. Ziwo’s successful transition from ISO 27001:2013 to ISO 27001:2022 certification demonstrates the value of combining advisory guidance, automation, and independent auditing.

With Axipro as their advisory partner, Drata providing automation, and Insight Assurance serving as the audit partner, Ziwo strengthened its compliance program while ensuring alignment with international best practices. The result was a smooth certification process, completed with zero non-conformities, and an internal team better prepared to manage compliance moving forward.

For organizations looking to upgrade or begin their compliance journey, Axipro provides advisory support to help teams align with updated standards, adopt automation tools, and prepare confidently for audits.

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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