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Narva Software SOC 2 Readiness in Record Time with Axipro

Featured Partner

Vanta

Product

SOC 2

Industry

IT Services and IT Consulting

Company size

2-10 employees

Location

Kerpen, Germany

Narva Software SOC-2 Readiness Axipro

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Narva Software, a leading Atlassian partner based in Germany, achieved SOC 2 readiness faster than expected, thanks to Axipro’s expert guidance and structured approach.
With a clear plan, hands-on support, and seamless collaboration, the Narva Software SOC 2 readiness journey became smooth, efficient, and stress-free.
If you’re preparing for SOC 2 and want a faster, less stressful path, this success story will show you how.

About Narva

Narva Solutions UG, known as Narva Software, is headquartered in Kerpen, Germany.
 The company builds innovative apps for Jira and Confluence, helping teams work smarter, collaborate better, and manage projects with greater efficiency.

Their solutions include:

  • Embedding external content into Confluence for richer documentation.
  • Exporting Confluence content quickly for sharing and reporting.
  • Enhancing Jira workflows with pre-built templates and labels.
  • Adding advanced capabilities to Confluence, such as LaTeX formula support.

Serving a global customer base, Narva Software is committed to delivering tools that make teamwork simpler and more effective.
When the time came to pursue SOC 2 compliance, they knew they needed a partner who could make the process clear, fast, and painless.

The Compliance Challenge

For Narva Software, achieving SOC 2 readiness was more than a checkbox. It was a way to strengthen customer trust, open doors to enterprise contracts, and demonstrate a strong commitment to data security.

However, the path to compliance came with challenges:

  • Understanding Vanta and configuring it for SOC 2 requirements.
  • Creating and refining the right security and operational policies.
  • Coordinating efforts without disrupting daily business operations.

They needed end-to-end guidance, a partner who could simplify the process while ensuring every requirement was met.
If this sounds familiar, you’re not alone. Many fast-growing companies face these same hurdles before they find the right compliance partner.

Why Narva Software Chose Axipro

Narva Software selected Axipro because of our proven record in helping companies achieve SOC 2, ISO 27001, HIPAA, and GDPR compliance.
As the Most Reviewed DRATA Partner, we are known for delivering results with speed, precision, and minimal disruption to business operations.

Our approach goes beyond simply “getting the badge.” We focus on building a compliance framework that strengthens operations and supports long-term growth.
For Narva Software’s SOC 2 readiness, they wanted a trusted partner who could own the process from start to finish, and that’s exactly what we delivered.

The Axipro Solution

We began by creating a structured, milestone-driven plan tailored to Narva Software’s timeline and business priorities.
Each stage was designed to make progress measurable and predictable.

Our team:

  • Guided Narva Software step-by-step through the Vanta platform.
  • Assisted in creating and refining the required SOC 2 policies.
  • Provided templates, best practices, and direct implementation support.
  • Coordinated closely with audit partner Johanson Group to ensure full readiness.

Because the plan was crystal clear, the Narva Software SOC 2 readiness process moved quickly, allowing their team to stay focused on building great products.
If you’ve been delaying compliance because it feels overwhelming, imagine what your team could accomplish with this kind of structured support.

Results Achieved

Narva Software reached full SOC 2 readiness faster than anticipated. The process delivered:

  • Well-documented and fully implemented security policies.
  • Confidence in meeting every SOC 2 requirement.
  • A smooth handoff to the audit partner with no last-minute issues.

With compliance in place, Narva Software is now positioned to attract more enterprise clients and strengthen its market credibility.
 Fast compliance, minimal disruption, and zero guesswork, that’s the Axipro difference.

Customer Satisfaction

Narva Software expressed genuine satisfaction with the results.
They appreciated how the SOC 2 readiness process was not only fast but also well-organized and easy to follow.
The team highlighted Axipro’s clear guidance, efficient use of the Vanta platform, and ability to keep the project on track without slowing down their core development work.

In their words, the journey to compliance felt “smooth, structured, and surprisingly quick” — exactly the outcome they were hoping for.

Your Compliance Success Story Starts Here

The Narva Software SOC 2 readiness success demonstrates what’s possible when expert guidance meets proven processes.
 At Axipro, we help businesses achieve SOC 2, ISO 27001, HIPAA, and GDPR compliance faster, with less stress, and without sacrificing productivity.

Whether you’re starting your first compliance project or preparing for a renewal audit, we can help you build the right roadmap and get you there with confidence.

Contact us today to begin your own compliance success story.

AI Governance Maturity Model: 5 Levels Explained

Pedro Dias July 31, 2026 8:40 am

Most organizations think their AI governance is further along than it is. McKinsey’s 2026 AI Trust Maturity Survey of roughly 500 organizations found an average maturity score of 2.3 out of 4, and only about a third reported level three or higher in strategy, governance, and agentic AI oversight. Adoption is outpacing control, and regulators have noticed. An AI governance maturity model gives you a way to measure that gap honestly. This guide covers what a maturity model is, the six dimensions it should measure, the five levels most models use, and how to assess your own organization and build a roadmap to the next level. What Is an AI Governance Maturity Model? An AI governance maturity model is a structured framework that describes how capable an organization is at governing its AI systems, usually across five progressive levels. The concept borrows directly from the Capability Maturity Model (CMM) that software engineering has used since the early 1990s: define the capability, describe what it looks like at each stage of development, and score yourself against it. The purpose is diagnosis. A maturity model tells you where governance is strong, where it’s theater, and where it doesn’t exist at all. How It Differs from General AI Governance Frameworks Frameworks like the NIST AI Risk Management Framework or ISO/IEC 42001 tell you what good governance contains: policies, risk assessments, accountability structures, monitoring. A maturity model tells you how well you’re doing those things today. The framework is the destination. The maturity model is the odometer. That distinction matters in practice. Plenty of companies can point to an AI policy document. Far fewer can show that the policy changes what teams actually ship. Why Enterprises Need a Maturity Model Three reasons. First, budget: you can’t prioritize governance investment without knowing which dimension lags. Second, accountability: a maturity score gives boards something concrete to track quarter over quarter. Third, regulation: the EU AI Act and frameworks like ISO 42001 assume a functioning management system, and a maturity assessment is the fastest way to find out whether yours would survive scrutiny. Core Dimensions of an AI Governance Maturity Model A useful model measures more than policy coverage. Six dimensions show up consistently across the credible models, including the IEEE-USA flexible maturity model built on the NIST AI RMF. Strategy and leadership. Does the organization have a stated position on AI risk, an executive owner (increasingly a Chief AI Officer), and board visibility? Gartner’s 2025 polling found 55% of organizations now have an AI board or dedicated oversight committee, which means nearly half still govern by improvisation. Policies, standards, and accountability. Written policies mapped to regulations, a RACI matrix for AI decisions, and clear escalation paths. Many organizations adapt the three lines of defense model from financial risk: the teams building AI, the risk function overseeing them, and internal audit checking both. Data governance and model lifecycle. Training data lineage, quality controls, and lifecycle management from development through deployment, monitoring, and retirement. This is where AI governance meets MLOps, and where mature organizations maintain an AI register, a live inventory of every model and system in production. Risk, compliance, and ethics. Risk classification of AI systems, impact assessments, bias and fairness testing, and explainability requirements. Banks will recognize the DNA of model risk management under SR 11-7 here. People, skills, and culture. Training, role clarity, and whether people outside the governance team actually understand their obligations. Tools, automation, and monitoring. Drift detection, automated policy checks, audit logging, and dashboards. Governance that lives in spreadsheets caps out around level three. The 5 Levels of AI Governance Maturity Level 1: Ad Hoc / Initial AI use happens without oversight. There’s no inventory, no policy, or a policy nobody follows. Shadow AI is common, and risk surfaces only when something breaks publicly. Level 2: Developing / Repeatable Someone has been assigned responsibility. A draft policy exists, a partial inventory exists, and reviews happen for high-profile projects. The practices are repeatable but depend on specific people rather than defined processes. Level 3: Defined / Structured Governance is documented, standardized, and applied across the organization. There’s a governance committee, a risk classification scheme, defined lifecycle gates, and mandatory training. Most organizations pursuing ISO 42001 certification are working to reach and formalize this level. Level 4: Managed / Metrics-Driven Governance produces numbers. Coverage rates, review cycle times, incident counts, and risk reduction are measured and reported to leadership. Controls are enforced by tooling rather than goodwill, and audits confirm the system works as described. Level 5: Optimized / Adaptive Governance improves itself. Monitoring feeds back into policy, controls adapt to new model types (agentic systems being the current test), and the organization anticipates regulatory change rather than reacting to it. Almost nobody is here yet, and that’s fine. Level 5 is a direction, not a deadline. Insider Note: In assessments, the most common self-scoring error is claiming level 3 on the strength of documents alone. If your policy says every model gets a pre-deployment review and your inventory shows 40 models but your review log shows 6, you’re at level 2. Evidence beats paperwork every time, and auditors check the logs first. AI Governance Maturity Matrix The matrix crosses dimensions with levels so you can score each one independently. Organizations are rarely uniform: it’s normal to sit at level 3 on policy and level 1 on monitoring. For scoring, keep the rubric simple: 1 to 5 per dimension, scored on evidence you could show an auditor, not on intentions. Board-level indicators (does the board see AI risk reporting?) and operational indicators (does every production model have a completed impact assessment?) should be scored separately, because they fail independently. How to Assess Your Current AI Governance Maturity Start with a baseline self-assessment. Pull together a cross-functional group covering engineering, legal, risk, security, and the business owners of major AI use cases, and score each dimension against the matrix. Half a day is usually enough for a first pass. For each dimension, the

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How Long Does It Take to Get ISO 42001 Certified?

Pedro Dias July 30, 2026 6:36 am

Most organizations get ISO 42001 certified in 2 to 9 months. Companies that already hold ISO 27001 regularly land in the 2 to 5 month range, while enterprises with sprawling AI portfolios and no existing management system can take 12 months or more. The audit itself only takes days. Almost the entire calendar goes into building and operating your AI Management System (AIMS) long enough to produce evidence an auditor can actually check. That is the short answer. The longer answer depends on your starting point, your scope, and how quickly you can get a certification body on the schedule. This article breaks down the full timeline phase by phase, the factors that stretch or compress it, and what the recertification cycle looks like once you hold the certificate. Typical ISO 42001 Certification Timeline at a Glance ISO/IEC 42001:2023 is the first international standard for AI management systems, published in December 2023. Because it follows the same harmonized structure as ISO 27001 and ISO 9001, the certification process will feel familiar to anyone who has been through a management system audit: build the system, run it, pass a Stage 1 and Stage 2 audit, then maintain it through annual surveillance. Here is how timelines typically break down by company size. Average Timeline for Small Businesses Small companies move fastest because scope stays contained. A startup with two or three AI systems, a handful of decision makers, and short approval chains can finish scoping in a week and get policies signed off in days rather than weeks. The realistic floor for a small business starting from scratch is around 3 months. With an existing ISO 27001 program and a compliance platform already collecting evidence, 2 months is achievable. Average Timeline for Mid-Sized Companies Mid-sized companies usually take 6 to 9 months. The AI inventory is growing, more departments are touching AI systems, and risk assessments have to cover more use cases. Coordination becomes the hidden cost: getting engineering, legal, and product to agree on an AI policy takes longer than writing the policy itself. Average Timeline for Enterprises Enterprises should plan for 9 to 12 months, sometimes longer. The main drivers are AI system sprawl across business units, longer procurement cycles for certification bodies, and audits that take more days. The Stage 2 audit for a large multinational can run two weeks or more on its own, and internal alignment before the audit takes far longer than the audit itself. Breakdown of the ISO 42001 Certification Timeline by Phase The phases below overlap in practice. Treat the durations as effort estimates for a reasonably resourced program, not a strict sequence. Phase 1: Scoping and Gap Analysis (2–4 Weeks) Everything starts with two questions: which AI systems are in scope, and how far is your current governance from what the standard requires? The gap analysis maps your existing policies and controls against the standard’s clauses and Annex A controls, and produces the project plan for everything that follows. Get the scope wrong here and every later phase inherits the mistake. Phase 2: AIMS Design, Leadership, and AI Policy Development (2–4 Weeks) This phase establishes the skeleton of the management system: the AI policy, governance roles, objectives, and the leadership commitments the standard requires. Executive sign-off is the gating item. The documents are not hard to write. Getting senior leadership to formally own AI governance is where programs stall. Phase 3: AI Risk and Impact Assessments (2–6 Weeks) ISO 42001 requires both AI risk assessments and AI impact assessments, and the distinction matters. Risk assessments look at what could go wrong for the organization. Impact assessments look at consequences for individuals and society, which is a newer discipline for most teams. This phase takes longer when you have many AI systems, high-risk use cases, or no prior methodology to adapt. The output feeds directly into your Statement of Applicability (SoA), the document that maps which Annex A controls you have selected and why. Insider Note: Impact assessments are where auditors probe hardest, because they are the most distinctive part of ISO 42001 compared with ISO 27001. A recycled security risk register with “AI” pasted into it will get picked apart in Stage 2. Build the impact assessment methodology properly the first time. Phase 4: Controls Implementation (2–10 Weeks) The longest phase. Here you implement the Annex A controls selected in your SoA: AI system lifecycle documentation, data governance for training data, human oversight mechanisms, transparency measures, supplier management for third-party AI, and so on. Duration depends almost entirely on the gap analysis results. Organizations with mature engineering practices often find they already do much of this and just need to document it. Organizations without formal AI development processes are building from zero. Phase 5: Documentation, Training, and Evidence Collection (2–8 Weeks) Certification requires proof that the system operates, not just that it exists on paper. That means records: training completion logs, risk assessment outputs, review meeting minutes, monitoring reports. This phase runs partly in parallel with implementation, but it cannot be compressed below a certain floor because auditors want to see evidence generated over time, not a folder of documents all created the week before Stage 1. Phase 6: Internal Audit and Management Review (2–4 Weeks) The standard requires an internal audit of the AIMS and a formal management review before the certification audit. This is your dress rehearsal. A good internal audit surfaces nonconformities while they are still cheap to fix. Skipping or rushing it is a false economy that shows up later as Stage 2 findings. Phase 7: Stage 1 Certification Audit (1–2 Weeks) The certification body reviews your documentation and assesses readiness for Stage 2. The audit itself takes 1 to 3 days for most organizations. The auditor examines your scope statement, AI policy, risk and impact assessment methodology, SoA, and internal audit results, then issues findings. The 1–2 week window covers the audit plus the report. Phase 8: Closing Nonconformities (2–4 Weeks) Almost every Stage 1 produces findings.

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