Free Assessment

Table of Contents

Reach SOC 2 Compliance in 6 Weeks or Less.

Request a Demo

Home

  /

  / Avoiding Common Pitfalls in SOC 2 & ISO 27001

Avoiding Common Pitfalls in SOC 2 & ISO 27001

Picture of Adeyinka Adeleke
Copy Link

Product

SOC 2, ISO 27001

Partner

Drata

Locations

Worldwide

common-pitfalls-soc-and-iso27001-axipro

Introduction

Achieving SOC 2 and ISO 27001 certification is a major milestone for growing companies. These frameworks prove to customers, investors, and regulators that your organization takes security and compliance seriously. But while the end goal is clear, the path is rarely straightforward.

In a recent webinar, Ali Hayat, Principal Consultant at Axipro, joined compliance experts from Drata to share the most common mistakes companies make when starting their compliance journey. The session revealed why so many organizations run into delays, wasted effort, and audit challenges, and how to avoid them.

At Axipro, the most reviewed Drata partner in EMEA, we specialize in helping businesses navigate SOC 2 and ISO 27001 with confidence. Our mission is simple: AXIPRO | AUDIT PARTNER — YOUR SUCCESS, OUR PRIORITY.

Why SOC 2 and ISO 27001 Compliance Trips Up Companies

SOC 2 and ISO 27001 are two of the most trusted compliance frameworks worldwide. SOC 2 demonstrates that your organization protects customer data according to strict security principles. ISO 27001 establishes a comprehensive information security management system (ISMS) recognized across industries and regions.

Both frameworks bring significant benefits, from faster sales cycles to stronger customer trust. But they also come with challenges. Companies often underestimate:

  • The effort required to define scope and controls.
  • The importance of readiness assessments.
  • The need for continuous monitoring, not just one-time fixes.

These challenges lead to delays, costly remediation, and even failed audits. The good news? With the right approach, most of these pitfalls can be avoided.

Pitfall #1: Failing to Define Scope Early

One of the biggest reasons compliance projects go off track is an unclear scope. Organizations often jump into SOC 2 or ISO 27001 without deciding:

  • Which systems and services are in scope?
  • Which business units or regions are included?
  • What customer expectations need to be met?

When the scope is vague or constantly changing, teams waste time implementing unnecessary controls or scramble to fix gaps late in the process. Worse, auditors may reject the scope entirely, forcing companies to redo large parts of their preparation.

How to avoid it:

  • Define the scope at the very start of your compliance project.
  • Involve leadership, IT, and operations to ensure alignment.
  • Document your scope and confirm it with your auditor or compliance partner before moving forward.

At Axipro, we guide clients through structured scope definition workshops, making sure nothing is overlooked. This early clarity saves weeks of effort and ensures your compliance project stays on track.

Pitfall #2: Skipping a Readiness Assessment

Many companies rush into compliance without a clear picture of where they stand. They assume existing policies and controls are “good enough”, only to discover major gaps during the audit. This mistake leads to last-minute fire drills, added costs, and sometimes audit failure.

How to avoid it:

  • Conduct a readiness assessment before starting your SOC 2 or ISO 27001 journey.
  • Identify gaps in policies, processes, and technical controls.
  • Build a remediation plan with realistic timelines.

A readiness assessment is like a compliance health check. It tells you exactly where you stand today and what needs to be done to pass tomorrow. At Axipro, we make this process simple, mapping controls across both SOC 2 and ISO 27001 so you don’t duplicate effort.

Pitfall #3: Weak Policy Documentation & Evidence

Writing policies is easy. Proving that you follow them is harder. Many organizations create documents that look good on paper but fail under audit review because they lack consistent evidence of practice.

Auditors don’t just want to see your incident response policy; they want logs of real incidents and proof of how they were handled. They don’t just want an access control policy; they expect user provisioning records and termination checklists.

How to avoid it:

  • Align policies with day-to-day practices.
  • Centralize your evidence so it’s easy to access during audits.
  • Review and update documentation regularly as your business grows.

At Axipro, we help clients implement evidence collection workflows that ensure documentation matches reality. This avoids surprises when auditors ask for proof.

Pitfall #4: Overlooking Vendor & Third-Party Risk

Third parties are often the weakest link in compliance. Many organizations focus on their own systems but ignore the risks introduced by vendors, cloud providers, software partners, or outsourced services.

Without proper oversight, a vendor’s poor security practices can jeopardize your entire audit. Auditors will expect to see vendor due diligence, risk assessments, and contractual obligations that align with SOC 2 and ISO 27001 standards.

How to avoid it:

  • Perform vendor risk assessments before onboarding new partners.
  • Include security and compliance clauses in contracts.
  • Monitor vendor performance and update risk profiles regularly.

At Axipro, we provide clients with vendor management frameworks that simplify third-party risk oversight. This not only supports compliance but also strengthens the overall security posture.

Pitfall #5: Treating Compliance as a One-Time Project

Many organizations view SOC 2 or ISO 27001 certification as a finish line. Once the certificate is issued, the compliance effort slows down until the next audit cycle arrives. This stop-start approach creates risk, inconsistency, and unnecessary stress.

Both SOC 2 and ISO 27001 demand continuous compliance. SOC 2 Type 2 requires evidence across a 6–12 month period. ISO 27001 expects ongoing monitoring and continuous improvement of your information security management system (ISMS).

How to avoid it:

  • Build compliance into daily operations, not just once a year.
  • Assign clear ownership for ongoing monitoring and reporting.
  • Use automation to track changes and flag issues in real time.

At Axipro, we encourage clients to view compliance as a living system, not a project with an end date. With the right approach, you stay audit-ready all year round, avoiding surprises and last-minute scrambles.

Expert Advice: How to Stay Audit-Ready

During the webinar, our panel emphasized one central truth: compliance doesn’t need to be painful if you prepare correctly.

Here are the steps every organization should take:

  1. Start with a readiness assessment to benchmark your current state.
  2. Define the scope clearly before making changes.
  3. Map controls across SOC 2 and ISO 27001 to avoid duplication.
  4. Centralize documentation and evidence in one place.
  5. Involve leadership and assign ownership for compliance tasks.
  6. Use automation tools to reduce manual lift and human error.
  7. Conduct regular internal reviews to maintain continuous compliance.

A Real-World Example

One Axipro client, a fast-growing SaaS company expanding into the EU market, initially planned to pursue SOC 2 only. Midway, a major customer, requested ISO 27001 certification as well. Without a clear scope or readiness plan, they risked delays of 6–9 months and losing the deal.

By partnering with Axipro, they:

  • Defined a dual-framework scope in two workshops.
  • Completed a readiness assessment that revealed 12 control gaps.
  • Mapped SOC 2 controls to ISO 27001, cutting work in half.
  • Used Drata automation to streamline evidence collection.

The result? The company achieved both SOC 2 and ISO 27001 certification in under 8 months, closing the customer deal and securing new revenue.

Key Takeaways for Your Compliance Journey

Avoiding pitfalls is not about shortcuts; it’s about preparation. To accelerate SOC 2 and ISO 27001 certification, you need clarity of scope, a readiness plan, strong documentation, vendor oversight, and a commitment to continuous compliance.

With the right partner, compliance becomes a growth enabler, not a roadblock.

Conclusion

Compliance doesn’t have to slow your business down. At Axipro, we specialize in helping companies across EMEA avoid the most common SOC 2 and ISO 27001 pitfalls. As the most reviewed Drata partner in the region, we combine expert consulting with automation to deliver results that stand up to audit.

AXIPRO | AUDIT PARTNER — YOUR SUCCESS, OUR PRIORITY.

Ready to simplify your compliance journey? Book a consultation with Axipro today and take the first step toward audit-ready certification.

Axipro Author

Picture of Adeyinka Adeleke

Adeyinka Adeleke

Copy Link

Blog Highlights

Explore More Articles

Read More Blogs

AI Governance Maturity Model: 5 Levels Explained

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

Read more

How Long Does It Take to Get ISO 42001 Certified?

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.

Read more

How InsightPlay Earned a “Perfect” ISO 27001 Certification with Axipro

How Axipro Guided Technovative Solutions & DigiProd Pass to ISO 27001
Read more