Table of Contents

Reach SOC 2 Compliance in 6 Weeks or Less.

  / ,

  / SOC 2 Compliance: Tips for SaaS Companies to Achieve And Maintain Certification

SOC 2 Compliance: Tips for SaaS Companies to Achieve And Maintain Certification

For any SaaS company, securing customer data is the foundation of building trust and winning new business. SOC 2 compliance has become the gold standard for data security, proving to clients and prospects that your organization takes the protection of sensitive information seriously.

Getting there takes preparation, accurate documentation, and continuous effort. It can feel overwhelming for first-timers, but with the right guidance, it’s entirely manageable and well worth it.

Here’s the key thing to understand up front: SOC 2 isn’t just about passing an audit. It’s about embedding a culture of security into your organization. In this guide, we’ll walk through actionable tips to help SaaS companies achieve and sustain SOC 2 certification, prepare effectively, avoid common pitfalls, and turn compliance into a genuine competitive advantage.

SOC 2 Compliance SaaS Companies

What Is SOC 2 Compliance?

Let’s start with the basics. SOC 2 (short for Service Organization Control 2) is a framework developed by the American Institute of Certified Public Accountants (AICPA). It evaluates how organizations manage customer data based on five Trust Services Criteria: Security, Availability, Processing Integrity, Confidentiality, and Privacy.

Compliance means demonstrating that your systems, policies, and processes meet the standard required to protect data. For SaaS companies specifically, SOC 2 gives customers confidence that their sensitive information is in safe hands.

It’s worth noting that there are two types of SOC 2 reports. A Type I report assesses your controls at a single point in time, while a Type II report evaluates how effectively those controls operate over a period (typically three to twelve months). Enterprise clients almost always want to see a Type II report, so keep that in mind as you plan.

Why Do SaaS Companies Need SOC 2?

SaaS businesses operate in a fiercely competitive landscape where trust is one of the biggest differentiators. Achieving SOC 2 certification satisfies customer demands and signals your commitment to data security, enhancing your reputation and instilling confidence in potential clients.

Beyond reputation, SOC 2 simplifies internal operations by introducing structured policies and standardized processes. This reduces the risk of data breaches, operational failures, and non-compliance, while improving overall system reliability. The strong controls you build to meet the criteria tend to make your entire organization run more smoothly.

Perhaps most importantly, SOC 2 opens the door to enterprise clients, who typically require very high compliance standards from their vendors. A clean report is often the difference between getting shortlisted and getting ignored, making it a powerful tool for closing high-value deals.

Reach SOC 2 Compliance in 6 Weeks or Less

Schedule Your Free SOC 2 Assessment Today

Preparing Your SaaS Company for SOC 2

Perform a readiness assessment. 
Before diving into the compliance process, evaluate where you stand. A readiness assessment surfaces the gaps in your existing controls, policies, and systems. Bring in an experienced auditor or consultant to review your current state and recommend the improvements you’ll need to make. Skipping this step is one of the most common reasons companies stumble later.

Define clear ownership. 
SOC 2 compliance is a team effort, but assigning ownership ensures accountability. Designate a compliance leader to coordinate activities, manage timelines, and communicate progress. This role keeps everyone aligned and ensures no detail slips through the cracks.

Define the scope. 
Clearly outline the scope of your audit, concentrating on the critical systems and processes that touch customer data. A well-defined scope helps you prioritize your efforts and avoid unnecessary complexity that can inflate both timeline and cost.

SOC 2 Compliance - SaaS Companies

Properly Implementing SOC 2 Controls

Develop detailed policies.
Create and implement policies covering data security, access management, incident response, and risk assessment. Each policy should map directly to the relevant SOC 2 Trust Services Criteria, ensuring a clear, auditable link between what you say and what you do.

Use automation tools.
Compliance automation platforms make the process dramatically easier by reducing manual effort and improving accuracy. These tools handle continuous monitoring, evidence collection, logging, and incident reporting. Popular solutions include Vanta and Drata, both of which can significantly shorten your time to certification. That said, the tool is only as good as the processes behind it, so don’t treat automation as a substitute for genuine security practices.

Train your team.
The success of any compliance program depends on a security-first culture. Regularly train employees on best practices, policy adherence, and their individual role in protecting information. Human error remains one of the leading causes of security incidents, so this step matters more than many companies realize.

Passing the SOC 2 Audit

Select the right auditor.
Choose a seasoned, reputable SOC 2 auditor who understands your industry and how SaaS businesses actually operate. The right partner will make the process far less painful, and Axipro can help guide your company through it from start to finish.

Prepare thorough documentation.
Auditors require extensive documentation of your policies, controls, and processes. Keep your records current and organized throughout the year rather than scrambling to assemble them at the last minute, this is where a lot of companies lose time.

Run internal testing.
Before the formal audit, conduct internal tests to verify the effectiveness of your controls. Proactively identifying and fixing issues ensures a far smoother audit and reduces the risk of unexpected findings.

Maintaining SOC 2 Compliance

Monitor continuously.
SOC 2 is not a once-and-done event, especially for a Type II report that measures performance over time. Continuously monitor your systems for vulnerabilities and confirm that your controls remain effective month after month.

Update policies regularly.
As your business operations evolve, so do your compliance needs. Review and update your policies to reflect changes in regulations, technology, and organizational structure.

Keep training on schedule.
Ongoing employee training ensures your team stays current on compliance changes, emerging security threats, and evolving best practices. A single annual session isn’t enough, treat this as a recurring commitment.

Advantages of SOC 2 Compliance for SaaS Companies

Customer confidence.
A SOC 2 report gives clients concrete assurance that your company takes data security seriously, building lasting trust and credibility.

Competitive edge.
Compliance puts your SaaS company ahead in the market and makes you a viable option for enterprise customers who won’t even consider vendors without it.

Operational efficiency.
The disciplined processes and controls you build along the way translate into fewer risks and a more efficient organization overall.

Final Thoughts

SOC 2 compliance is a strategic investment, one that builds trust, wins enterprise deals, and strengthens your data security posture. The tips above should give you the confidence to pursue and sustain certification.

Just remember: compliance isn’t a destination; it’s an ongoing journey of monitoring, updating, and training your team to stay ahead of evolving threats.

Frequently Asked Questions

Why is SOC 2 compliance important for SaaS companies?

SOC 2 ensures SaaS companies adhere to rigorous data security standards, building trust with customers while minimizing operational and reputational risk.

It depends on your company’s size and readiness, but the process generally takes anywhere from three to twelve months.

The five criteria are Security, Availability, Processing Integrity, Confidentiality, and Privacy. Security (the “common criteria”) is mandatory; the others apply based on your scope.

Not strictly, but tools like Vanta and Drata make compliance significantly easier, improving both efficiency and the accuracy of your controls.

Focus on continuous monitoring, regular policy updates, and ongoing employee training to sustain your compliance over time.

Axipro Author

Picture of Pedro Dias

Pedro Dias

Pedro has been writing online for over 10 years. With experience in all things programming, cyber security, and compliance, he is our editor-in-chief at Axipro.

Blog Highlights

Explore More Articles

For the past two years, enterprise AI risk conversations have centered on a familiar set of concerns: model bias, hallucination, data privacy, and dependency on third-party models. These are real risks, and most organizations now run some version of a governance program to manage them. But something has shifted. Organizations are no longer just deploying AI that generates content for a human to review. They’re deploying AI that acts. Agents now plan multi-step tasks, call APIs, move data between systems, execute transactions, and coordinate with other agents, often with no human checkpoint in the loop. That shift deserves more than a footnote in the existing AI risk category. It deserves its own line in the risk register: Agentic Autonomy Risk. What Is Agentic AI Risk Management? Agentic AI risk management is the practice of identifying, assessing, and controlling the risks created when AI systems take autonomous action on an organization’s behalf. Where traditional AI governance evaluates outputs (accuracy, bias, privacy), agentic AI risk management governs what agents actually do: the tools they call, the permissions they inherit, and the downstream consequences of their actions. That distinction is the reason existing risk registers struggle with agents, and it’s worth unpacking properly. What Agentic AI Actually Changes Traditional AI systems, even generative ones, are advisory. They produce an output such as a summary, a prediction, a draft email, or a classification, and a human remains the last checkpoint before anything happens in the real world. Agentic AI removes that checkpoint. An agentic system doesn’t just produce an answer. It pursues a goal. It decides which tools to call and in what order, then executes those actions directly against live systems: submitting a purchase order, modifying a database record, sending an external communication, or orchestrating a set of sub-agents to complete a broader workflow. Agentic autonomy is the degree to which a system can plan and execute actions without a human explicitly authorizing each step. It’s a spectrum rather than a binary. At one end, the AI drafts and a human approves every action. At the other, the AI operates within broad guardrails and only escalates exceptions. The further an organization moves along that spectrum, the less its exposure looks like software risk and the more it looks like delegated authority risk, the kind normally reserved for employees, contractors, and automated financial systems. Why Existing Risk Registers Miss Agentic AI Risks Most enterprise risk registers were built on a reasonably safe assumption: a human initiates consequential actions, and the technology around that human behaves deterministically. Agentic AI breaks both halves of that assumption at once. A few specific gaps show up quickly when organizations try to map agentic deployments onto existing categories. Operational risk registers assume process failures come from human error or system outages, not from a system independently choosing an unanticipated path to a stated goal. Cybersecurity risk registers are built around unauthorized external access, while an agent problem usually involves an authorized system taking unauthorized internal actions with its own legitimate credentials. Model risk frameworks, borrowed largely from financial services, evaluate output accuracy rather than action consequences, which matters most when those actions can’t be reversed. And third-party risk assessments treat vendors as static entities, not as autonomous agents that might invoke other vendors’ agents on your behalf. See our guide to the NIST AI Risk Management Framework for how output-focused frameworks are structured. The result is a governance blind spot. An organization can be compliant against its AI policy, its cybersecurity policy, and its vendor risk policy, and still have nobody accountable for the specific risk of a system initiating a harmful sequence of actions before anyone notices. Defining Agentic Autonomy Risk Agentic Autonomy Risk is the risk that an AI system, operating with delegated decision-making and execution authority, takes actions that are harmful, non-compliant, or misaligned with organizational intent before adequate human oversight can intervene. Those actions might happen independently or in coordination with other agents. It deserves standing as a named category alongside cybersecurity, operational, legal, financial, and third-party risk because the loss event itself is different. The harm is a completed action in a live system, and it may be difficult or impossible to reverse. The accountability structure is different too: when an orchestrating agent delegates to sub-agents, responsibility for the outcome gets distributed in ways existing ownership models don’t cleanly capture. So is the detection window. Traditional controls assume a human is positioned to catch an error before it compounds, but an agent can execute dozens of dependent actions faster than any human review cycle. 7 Agentic AI Risk Scenarios to Put on Your Register 1. Unauthorized autonomous decision-making. An agent takes an action within its technical permissions but outside its intended business mandate. It adjusts pricing, approves a refund, or modifies a customer record, and no policy ever explicitly authorized that scenario. 2. Goal misalignment. The agent optimizes for a literal interpretation of its objective in a way that diverges from actual business intent, particularly under ambiguous or adversarial inputs. 3. Multi-agent interactions and cascading failures. One agent’s flawed output becomes another agent’s trusted input. A single error can propagate across a chain of agents faster than anyone can detect it, amplifying the original mistake instead of containing it. 4. Excessive tool or system permissions. Agents get provisioned with broad, standing access “to be safe” rather than scoped, least-privilege access tied to specific tasks. A productivity tool quietly becomes a privilege-escalation path. 5. Regulatory non-compliance. Autonomous actions trigger obligations under data protection, financial services, employment, or sector-specific regulation, and they execute without the compliance review a human-initiated process would normally receive. 6. Explainability and accountability gaps. An autonomous action causes harm and the organization can’t clearly reconstruct why the agent chose that path, or establish whether the business owner, the AI governance function, or the vendor is accountable for the outcome. 7. Autonomous third-party actions. A vendor’s agent, integrated into your environment, takes action on your behalf, or your agent acts against a

A SOC 2 penetration test costs between $1,000 and $30,000 for most companies. A typical SaaS scope, meaning one web application, its API layer, and the cloud infrastructure behind it, usually lands between $2,000 and $20,000. Early-stage startups with a narrow scope can get an auditor-accepted test for $1,000 to $8,000, while enterprises with multiple products and hybrid infrastructure regularly spend $20,000 to $50,000 or more. The spread is wide because “penetration test” covers everything from an automated scan with a cover page to weeks of manual testing by senior engineers. Auditors know the difference, and so do the enterprise customers who asked for your SOC 2 report in the first place. This guide breaks down what drives the price, where the hidden costs sit, and how to buy a test that holds up in fieldwork without overpaying for it. What Is SOC 2 Penetration Testing?​ A SOC 2 penetration test is a simulated attack on your systems, performed by a qualified security professional, scoped to the environment covered by your SOC 2 report. The tester tries to exploit real weaknesses the way an attacker would: broken access controls, injection flaws, misconfigured cloud services, exposed credentials. The output is a report your auditor reads as evidence that your security controls work in practice, not only on paper. That last part matters. A pentest bought for SOC 2 has a second audience beyond your security team. If the report doesn’t map findings to your audit scope, document its methodology, and show remediation, it fails the job you bought it for. We cover the full deliverable in our guide to what a SOC 2-ready VAPT report includes. How Penetration Testing Fits Into SOC 2 Compliance​ SOC 2 is built on the AICPA’s Trust Services Criteria, and the Security category (the Common Criteria) applies to every report. Penetration testing is the standard way to satisfy CC7.1, which expects you to detect and monitor for new vulnerabilities, and it supports CC4.1, which covers ongoing evaluations of whether controls actually function. The AICPA’s points of focus explicitly mention vulnerability scanning and penetration testing as examples of how companies meet these criteria. In practice, the test slots into your audit timeline as an evidence item. Your auditor will ask for the report, check the test date against the audit period, and review how you handled the findings. Remediation is often scrutinized harder than the test itself, because it shows whether your vulnerability management process runs or merely exists. Is Penetration Testing Required for SOC 2?​ Strictly speaking, no. The Trust Services Criteria never use the word “mandatory” about penetration testing. You could theoretically satisfy CC7.1 with vulnerability scanning and strong monitoring alone. In reality, almost every auditor expects one, and skipping it invites two problems. First, your auditor may push back during fieldwork or add exceptions to the report. Second, the enterprise buyers reviewing your SOC 2 report increasingly look for pentest evidence specifically, and a report without it raises questions during procurement. Treat the test as effectively required and budget for it from the start of your SOC 2 compliance checklist. How Much Does SOC 2 Penetration Testing Cost? Typical Price Range for SOC 2 Pen Testing Most companies pay $1,000 to $30,000, with the median engagement for a SaaS business sitting around $12,000 to $15,000. Compliance-focused tests at the lower end of the market start around $1,000 to $5,000. Deep manual testing from established firms runs $10,000 to $30,000. Anything quoted below roughly $3,000 is almost certainly automated scanning packaged as a pentest, which auditors are getting better at spotting. Cost by Company Size (Startup, SMB, Enterprise) Company size is a proxy, not the driver. A 15-person company with three products and a legacy on-prem component will pay more than a 200-person company with one tightly scoped SaaS platform. Testers price effort, and effort follows scope. Cost by Test Type (Network, Web App, API, Cloud, Internal/External) Most SOC 2 engagements bundle two or three of these. The common package for a cloud-native SaaS company is web app plus API plus cloud configuration, which is why the $1,000 to $20,000 band comes up so often. Companies with office networks and internal systems in their audit scope add internal network testing, and the price climbs accordingly. Factors That Influence SOC 2 Penetration Testing Cost Scope and Number of Assets Tested Scope is the single biggest cost driver. Every additional application, API endpoint group, cloud account, or network segment adds testing hours. A pentest priced without a scoping call is a pentest priced on guesswork, and the guess usually favors the vendor. Complexity of Application or Infrastructure​ A simple CRUD app with two user roles tests quickly. A multi-tenant platform with role hierarchies, workflow engines, file processing, and third-party integrations takes far longer, because each of those features creates attack surface a tester has to work through manually. Authentication tiers matter especially: every distinct role needs testing for privilege escalation and cross-tenant data access. Testing Methodology (Black Box, Grey Box, White Box) Black box testing gives the tester nothing but a URL, grey box adds credentials and documentation, and white box adds source code and architecture diagrams. Grey box is the default for SOC 2 and usually the best value, since the tester spends time exploiting rather than discovering. White box costs more upfront but finds deeper issues. Black box sounds rigorous but often wastes paid hours on reconnaissance an attacker would run for free. Depth of Testing and Manual vs. Automated Approaches Automated scanning finds known vulnerability patterns. Manual testing finds business logic flaws, chained exploits, and authorization gaps that no scanner catches, and it’s the part auditors and security-literate customers actually value. The ratio of manual work to automation is the honest explanation for most price differences between two quotes covering the same scope. Tester Credentials and Firm Reputation Senior testers holding OSCP, GPEN, or CREST credentials bill higher rates, and firms with recognized methodologies charge a premium for the credibility their letterhead carries

Two compromised versions of LiteLLM sat on PyPI for roughly 40 minutes on the morning of March 24, 2026. That window was enough to capture secrets from around 434,000 CI/CD pipeline runs across nearly 2,500 organizations, including AWS, Samsung, Cisco, Salesforce, Siemens, and Deloitte. In August, researchers at CloudSEK and Hudson Rock confirmed they had obtained the raw exfiltrated data: a 153GB archive containing 433,909 files of environment variables, cloud keys, Kubernetes secrets, and API tokens harvested live from running pipelines, as covered by Help Net Security’s reporting on the credential archive. If LiteLLM runs anywhere in your stack, or you touch any AI proxy infrastructure at all, you need answers to three things: whether you were exposed, what to rotate first, and whether the rotation you did back in March actually held. That last one matters more than it sounds, because “we rotated everything” has already burned at least one very large company. How the Breach Happened The attack didn’t start with LiteLLM. On March 19, 2026, a threat group called TeamPCP compromised the build pipeline of Trivy, a vulnerability scanner half the industry runs, and pushed a poisoned release. LiteLLM’s own CI pipeline ran Trivy, so the poisoned scanner had legitimate read access to the project’s runner environment. The attackers used that to steal LiteLLM’s PyPI publishing tokens and ship two malicious releases of their own: versions 1.82.7 and 1.82.8. KICS and the Telnyx Python SDK got hit in the same campaign. The payload design is the part worth studying. The malicious package dropped a .pth startup hook into site-packages, so the code ran the moment any Python interpreter started on the machine, whether or not anything imported LiteLLM. From there it harvested environment variables, read local credential files like .aws/credentials and .kube/config, tried to move laterally across Kubernetes clusters, and installed a systemd backdoor dressed up as a generic telemetry service. InfoQ’s coverage of the PyPI compromise put downloads of the compromised release above 40,000. For scale, LiteLLM normally gets downloaded around 3 million times a day. The exfiltration had a nasty fallback, too. According to CloudSEK, stolen data was encrypted and sent to a typosquatted domain, and when that failed, the malware created a public repository inside the victim’s own GitHub account and uploaded the loot as a release asset. Some companies were publishing their own secrets to the open internet and had no idea. Worth Knowing: The malicious code only existed in the PyPI artifacts. The GitHub source repository stayed clean the whole time, so a developer reviewing the code on GitHub saw nothing wrong. Source review isn’t artifact verification. If you don’t check that what the registry serves matches the upstream source, this class of attack is invisible to you. How to Check If You Were Exposed Three checks, from quickest to most involved. 1. Confirm whether the compromised versions ever ran The malicious versions went live on PyPI at 10:39 UTC on March 24, 2026 and got quarantined about 40 minutes later. The project’s advice: treat any install from that day before 16:00 UTC as suspect. Search your lockfiles, pip caches, SBOMs, and container image histories for 1.82.7 and 1.82.8. And check your internal artifact mirrors. An Artifactory or Nexus proxy that cached the bad release in March can keep serving it internally long after PyPI pulled it. Keep the .pth mechanism in mind when you scope this. The question isn’t “which applications import LiteLLM,” it’s “which machines had the package installed at all,” because every Python process on an infected machine triggered the payload. 2. Hunt for persistence Rotation is pointless if the attacker still has a foothold. Check developer machines, CI runners, and containers for unauthorized .pth files in site-packages and for suspicious systemd units, especially anything posing as a system telemetry service. And review activity from March 24 onward, not just the 40-minute window. Persistence is there so the access outlives the infection. Pro Tip: Don’t limit the persistence hunt to live machines. Base container images rebuilt in late March may have baked the payload into every image derived from them since. Scan your image registry for the affected LiteLLM versions and for unexpected .pth files, then trace which running workloads came from flagged images. 3. Check whether your secrets are in the dump Hudson Rock has published a domain lookup tool and is running ethical disclosures for affected organizations, and CloudSEK maintains a high-confidence victim list. Use them, but know their limits. Attribution in this dataset is genuinely hard. One dump with a siriusxm.com committer email actually traced, through its self-hosted GitLab endpoints, to AdsWizz, a SiriusXM subsidiary. And a large share of the dumps are generic pipeline configurations with no identifying domain, email, or server name at all. Absence from a victim list is not evidence of absence. If your pipelines ran the compromised versions, assume exposure no matter what a lookup tool tells you. What to Rotate, in What Order The guidance from both research teams is blunt: treat every secret the LiteLLM environment could reach as compromised. That covers secrets on disk, in memory, injected into CI jobs, and anything retrievable through instance metadata services. Work down by blast radius: Priority Credential type Why it comes first 1 Cloud IAM keys (AWS, GCP, Azure) Direct control of infrastructure, data stores, and billing. This is where attackers monetize fastest. 2 GitHub and GitLab PATs, package publishing tokens These let an attacker poison your releases and turn your company into the next link in the supply chain. 3 Kubernetes service account tokens and kubeconfigs Lateral movement across clusters was built into the payload, not a theoretical risk. 4 Database passwords and third-party API keys Dumped in plain text in the archive, often with no attribution, so nobody will warn you they leaked. 5 AI provider API keys Billing abuse, quota theft, and access to whatever data flows through your LLM routing layer. One word matters more than the rest of this article: revoke, don’t just rotate. That