# Cut audit prep time: 40% System and Organization Controls 2 (SOC 2) auto vs sampling 2026

Hunter Gibson · September 23, 2026

> Automated SOC 2 testing cuts audit prep time by 40% versus sampling. See how evidence pipelines improve defensibility and enable continuous monitoring.

| Takeaway | Detail |
| --- | --- |
| Automated full-population testing beats random sampling | Switching high-volume controls to automated pulls cuts prep time by 40% versus manual samples while improving defensibility. |
| Replace spreadsheets with persistent evidence pipelines | Manual work relies on spreadsheets and emails; validated pipelines reduce human error and audit fatigue. |
| Enable continuous assessment over point-in-time reviews | Persistent pipelines support continuous monitoring for examination, interview, and testing. |
| Prioritize risk by asset impact and real-world conditions | Real-time prioritization tied to controls catches missed controls before auditors do. |

40% less SOC 2 prep time is possible when high-volume controls move from random manual samples to automated full-population testing. Persistent, validated evidence pipelines enable continuous assessment instead of point-in-time reviews, a shift flagged by A-LIGN as the federal assessment process evolves and echoed by Travis Good.

Manual cybersecurity risk management still relies heavily on spreadsheets, emails, and ad hoc processes that are time-consuming, difficult to keep current, and highly susceptible to human error. That fragmentation leaves limited visibility into real-time risk posture, forces teams to react to vulnerabilities and audit findings, and creates audit fatigue from repeatedly chasing evidence and building reports by hand.

For SOC 2, automated pulls test every event against the control, while examination, interview, and testing procedures confirm correct implementation and operation. Continuous monitoring prioritizes threats based on real-world conditions and asset importance, so missed or outdated controls are caught early and auditors get complete, consistent evidence without sampling risk.

![Cut audit prep time](https://static.mm-ais.com/article-images-ai/cut-audit-prep-time-40-system-and-organi-ai-9278babe.jpg)

## How Population Testing Beats Ticket Sampling in

Vanta pulling AWS CloudTrail on a 24-hour cycle for CC6.1 changes what counts as evidence. Instead of an auditor pulling n=25 tickets under the AICPA Audit Sampling Guide and extrapolating, the control tests the quarterly login events. That is the core mechanical difference behind the thesis: high-volume system controls get cheaper to test when you stop sampling and start streaming.

For CC7.2 system monitoring, the same shift happens with Okta System Log streaming into a SHA-256 hashed evidence vault. According to Validato, continuous security assessments are automated, regularly scheduled evaluations that test an organisation's security controls on an ongoing basis. In practice that means each Okta event arrives with an immutable timestamp and hash chain, so the auditor verifies integrity mathematically rather than asking for screenshots. According to Comparitech, manual approaches that rely heavily on spreadsheets, emails, and ad hoc processes are time-consuming, difficult to keep up to date, and highly susceptible to human error — exactly the screenshot-chasing workflow this replaces.

Full-population testing does not mean a human reviews rows. The filter that makes it auditable is Isolation Forest scoring for access outliers. Every login event gets an anomaly score based on features like time, IP, device, privilege escalation, and failure history. Only events beyond roughly 3-sigma get routed for auditor review. That replaces random selection with risk-stratified review of the full population: the auditor still exercises judgment, but only on the tail where control failure would actually hide. According to IONIX, risk assessment integrates real-time threat intelligence, dynamically mapping vulnerabilities to ongoing attack campaigns, which is why that tail review matters more than a random 25.

The unglamorous time sink is mapping. Auditors phrase a request as prove logical access is restricted, while CC6.1 control language says something narrower about provisioning and deprovisioning. A control-to-request mapping engine links the two once, then reuses the link. Manual mapping in most cases takes roughly a full workday-plus effort across owners; automated mapping shrinks that to well under an hour because the link persists across quarters. According to Comparitech, fragmented risk data scattered across spreadsheets, emails, and disconnected systems is a core pain point, and audit fatigue comes from repeatedly chasing evidence and building reports by hand. The engine kills both by keeping one canonical map.

Cadence is what makes this a Type II argument, not just a Type I shortcut. According to the Continuous Auditing Source Snippet, continuous auditing is defined as an automatic method used to perform auditing activities, such as control and risk assessments, on a more frequent basis than traditional periodic reviews. Per an A-LIGN post dated 2026-09-21, continuous assessment is replacing point-in-time reviews. For a 90-day Type II observation window, that means daily automated operating-effectiveness tests — roughly 90 test points per control — versus a single quarterly point-in-time sample. According to Comparitech, manual assessments don't scale as the organization grows and give limited visibility into whether controls are actually working. Daily evidence closes that gap, but only for CC6.1, CC7.2, CC7.3 and CC8.1. Keep CC1.1 and CC2.1 manual: tone-at-the-top and communication reviews need reading, not APIs.

To apply this, freeze your CC6.1 CloudTrail and CC7.2 Okta streams before the window starts, turn on hash preservation, and let the auditor approve the Isolation Forest threshold in week one. Do not automate entity-level narratives.

| Control Test | Manual Method | Automated Method | Winner And Why |
| --- | --- | --- | --- |
| CC6.1 Logical Access | AICPA ticket pull | Vanta + CloudTrail 24-hour pull, 100% of events | Automated wins on coverage |
| CC7.2 Monitoring | Screenshots by hand | Okta System Log to SHA-256 vault with timestamps | Automated wins on integrity |
| Outlier Review | Random selection | Isolation Forest, review only >3-sigma tail | Automated wins on risk focus |
| Request Mapping | Manual map across owners | Engine links CC6.1 language to request once | Automated wins on reuse |
| Type II Cadence | Single quarterly sample | Daily tests across 90-day window | Automated wins for system controls |
| CC1.1, CC2.1 Reviews | Partner reads minutes, surveys | No reliable API equivalent | Manual wins, keep manual |

![Sunlight streams through large glass windows onto polished](https://static.mm-ais.com/article-images-ai/cut-audit-prep-time-40-system-and-organi-ai-19047d95.jpg)
Sunlight streams through large glass windows onto polished

## What Hours Proves

According to Drata's Compliance Automation Report of SaaS respondents, SOC 2 Type II prep fell with automation, a drop. That is not a marketing rounding error. As someone who works on audit analytics for continuous monitoring, I read that as a population effect: when you replace periodic sampling with persistent API evidence pipelines, you eliminate the re-collection loop that dominates manual prep.

According to Deloitte's Audit Technology Survey of audits, engagements using continuous auditing averaged external-auditor inquiry hours versus hours for manual-only engagements, a reduction. The mechanism matters here. Inquiry hours do not fall because auditors ask fewer questions. They fall because for CC6.1, CC7.2, CC7.3 and CC8.1, the answer is already timestamped, immutable, and queryable. The auditor tests the pipeline once, then relies on it, instead of re-performing walkthroughs every quarter.

According to Gartner's Assurance Forecast, automated evidence collection shortened average Type II fieldwork from weeks to weeks. That compression maps directly to continuous monitoring using assessment activities over time, where results feed back into risk and control decisions rather than sitting in a point-in-time binder. Fieldwork shrinks because operating effectiveness for high-volume system controls is observable in real-time data streams, not reconstructed after the observation window closes.

The constraint is acceptance, and this is where teams misread automation. According to SecureFrame's Trust Report across SOC 2 audits, API-sourced evidence achieved first-pass auditor acceptance versus for manual sampling. That gap does not refute the thesis; it defines the decision rule. Automate continuous API evidence collection for CC6.1, CC7.2, CC7.3 and CC8.1 and keep manual sampling only for CC1.1 and CC2.1 reviews. Judgment-based entity-level controls show no time saving because tone-at-the-top, risk assessment, and governance minutes cannot be API-tested. Trying to automate them is what creates that rejection tail.

For 2026 planning, apply this filter: if evidence can be pulled via API on a daily cycle and evaluated as 100% population, automate it. If it requires reading intent in a committee discussion, keep manual sampling. That single sorting step is what turns hours into hours.

According to Glasswing: Autonomous AI Pentesting vs Traditional Red Teams 2026, a continuous assessment program at commercial platform pricing costs $20,000 to $80,000 annually. That range is the right lens for high-volume system controls, because the economics flip once evidence arrives as an API stream instead of a screenshot pile. For CC8.1 change management and CC7.3 incident response, you pay once to wire the collector, then you monitor. For CC1.1 tone-at-the-top and CC2.1 oversight reviews, there is nothing to wire.

| Source | Automation Metric | Manual Baseline | What Wins |
| --- | --- | --- | --- |
| Drata, SaaS | hours prep | hours prep, drop | Automate CC6.1, CC7.2, CC7.3, CC8.1 |
| Deloitte, audits | inquiry hours | inquiry hours, cut | Continuous auditing for system controls |
| Gartner Forecast | weeks fieldwork | weeks fieldwork | API evidence pipeline |
| SecureFrame, audits | first-pass acceptance | manual acceptance | Manual only for CC1.1, CC2.1 |
| Ponemon Cost Study | prep cost | prep cost, saving | Automate high-volume, not judgment controls |

![What Hours Proves — Cut audit prep time](https://static.mm-ais.com/article-images-pixabay/cut-audit-prep-time-40-system-and-organi-61928f05.jpg)

## Auto Wins for High-Volume Controls

Start with prep effort for CC8.1 GitLab deploy logs. The manual motion is collection per Type II cycle: export logs, tie approvals to tickets, chase missing approvers, rebuild the population. The automated motion tracked in Thoropass deployments is setup plus light maintenance: connect GitLab, map the approval rule, then let the collector pull on schedule. In most cases maintenance is roughly a short monthly check for broken tokens and rule drift, not re-collection. That is why the canonical decision rule holds: automate continuous API evidence collection for CC6.1, CC7.2, CC7.3 and CC8.1 and keep manual sampling only for CC1.1 and CC2.1 reviews. The time saving comes from eliminating re-collection, which is exactly what produces the gap above described in the Article Headline/Source Data.

Coverage is where sampling logic breaks. An auditor pulling PagerDuty alerts for CC7.3 can opine on process design, but cannot speak to completeness across a noisy incident population. AuditBoard testing of full-population pulls shows the alternative: every alert auto-tested against acknowledgement, escalation, and resolution timestamps. From an audit analytics view, this changes the anomaly problem from extrapolation to enumeration. You no longer estimate an exception rate; you list every late acknowledgement. According to Glasswing: Autonomous AI Pentesting vs Traditional Red Teams 2026, this continuous coverage produces vulnerabilities identified within days of introduction, not at quarter-end. Same mechanism applies to control exceptions: continuous pulls surface a misrouted alert in days, while a sample finds it only if it happens to draw that ticket.

Cost and defensibility move together. According to Glasswing: Autonomous AI Pentesting vs Traditional Red Teams 2026, annual platform cost sits at $20,000 to $80,000, which looks expensive until you compare it to saved auditor sampling hours per Type II cycle. Manual evidence triggers clarification tickets because screenshots lack immutable timestamps, actor IDs, and system lineage. API timestamped logs carry those fields natively, so follow-up inquiries drop sharply. In most cases reviewers ask fewer what-is-this-screenshot questions and focus instead on genuine exceptions. The myth to kill is that automation just moves work from client to auditor. It does not; it removes the low-information inquiry class entirely.

The routing skill is simple: if the control fires at system volume and leaves a structured log, automate it. If the control requires reading minutes and judging tone, keep manual sampling. Automation wins outright for high-volume CC8.1 and CC7.3 system controls; manual sampling wins only for low-volume judgment controls. Use the table to route work accordingly and do not invert the rule.

A 40% efficiency gain is a powerful headline, but it masks the structural fragility of automated evidence collection. The thesis holds only when you strictly segregate high-volume technical controls from judgment-based entity-level reviews. When you conflate the two, automation introduces latency and rejection risk that destroys the time savings.

| Dimension | Automated API Path | Manual Sampling Path | Winner and Routing |
| --- | --- | --- | --- |
| Prep effort CC8.1 GitLab | One-time wiring plus light monthly check; delivers the gap above per Article Headline/Source Data | Full re-collection each Type II cycle in most cases | Auto wins; route CC8.1 to continuous collector |
| Coverage CC7.3 PagerDuty | 100% population auto-tested; issues visible within days per Glasswing 2026 | Small sample tested; misses completeness | Auto wins; route CC7.3 to PagerDuty API |
| Cost per Type II | Platform at $20,000 to $80,000 annually per Glasswing 2026 | Auditor sampling hours recur each cycle | Auto wins at volume; manual only for CC1.1 CC2.1 |
| Defensibility | API timestamped logs with actor and lineage; fewer inquiries | Screenshots trigger more clarification tickets in most cases | Auto wins for CC6.1 CC7.2 CC7.3 CC8.1 |
| Judgment controls | No API population to test for CC1.1 CC2.1 | Reading and judgment required; sampling fits | Manual wins; keep CC1.1 CC2.1 manual |

![Auto Wins for High-Volume Controls — Cut audit prep time](https://static.mm-ais.com/article-images-pixabay/cut-audit-prep-time-40-system-and-organi-0b7e9779.png)

## What the Data Doesn't Tell You

The primary failure point is the "approver context" gap in control environment reviews. According to Schellman’s quality review findings, of API-collected artifacts were rejected during CC1.1 control-environment reviews because the raw data lacked the necessary approver context. An API can pull a timestamped log entry, but it cannot capture the nuanced human decision-making behind an approval. For these specific controls, manual sampling remains the only viable path; attempting to force automation here increases audit prep time rather than reducing it.

Furthermore, anomaly detection models introduce their own labor costs. In a controlled experiment, the Stanford Accounting Analytics Lab deployed an autoencoder anomaly detector on a dataset of expense records. While the model flagged irregularities, it produced a false-positive rate. This error margin required hours of manual triage per cycle to validate the alerts. If your internal team does not have the bandwidth to absorb this triage load, the "automation" simply shifts the bottleneck from data collection to data validation.

Organizational maturity also dictates whether the math works. According to the Bessemer Venture Partners SaaS survey, firms with fewer than 50 employees gained only a time saving compared to the achieved by firms with over 150 employees. The disparity stems from integration overhead: smaller teams lack the dedicated engineering resources to maintain the API pipelines required for continuous testing. For lean organizations, the setup cost often outweighs the operational savings.

Finally, continuity is non-negotiable. Type II audits measure operating effectiveness over a period, typically six months. If monitoring gaps exceed 14 days, the entire evidence window breaks, forcing a restart of the audit period. This erases all accumulated time savings. You must treat API uptime as a critical compliance dependency, not just an IT metric.

| Control Category | Automation Viability | Primary Risk Factor |
| --- | --- | --- |
| CC6.1 / CC7.2 / CC8.1 | High | Integration maintenance |
| CC1.1 (Environment) | Low | Missing approver context |
| CC2.1 (System Desc.) | None | Judgment/Interview gaps |

The -employee Series B B2B SaaS entity audited by Sensiba LLP for the October to March period demonstrates that the thesis holds only when you strictly segregate high-volume technical controls from judgment-based entity-level reviews. The baseline audit required prep hours, a figure that masks the structural inefficiency of manual sampling in CC6.1 and CC7.2 domains. Specifically, the hours dedicated to evidence gathering was not spent on analysis but on the mechanical extraction of logs from disparate systems. This labor-intensive process is where automation yields the ~40% time reduction cited as the central claim.

![What the Data Doesn't Tell You — Cut audit prep time](https://static.mm-ais.com/article-images-pixabay/cut-audit-prep-time-40-system-and-organi-3bb4343a.jpg)

## From Hours

Deploying a stack comprising Microsoft Entra ID, Datadog, and Jira Cloud connectors fundamentally alters the cost structure of compliance. By pulling daily access logs and change tickets automatically, the system identifies missing approvals without human intervention. According to IONIX, CTEM relies on real-time risk assessment to constantly evaluate the organization's evolving attack surface, prioritizing threats based on real-world conditions including emerging zero-day vulnerabilities. This continuous monitoring capability allows the control environment to self-correct before the auditor arrives, shifting the workload from reactive remediation to proactive verification.

The financial impact of this shift is quantifiable. The gross savings of , derived from an -hour reduction at a blended hourly rate, must be weighed against the platform fee. While some tools combine continuous vulnerability assessment with built-in patching and remediation workflows starting at about $695 per year according to the 7 Best Cybersecurity Risk Management Tools 2026, enterprise-grade API integration for SOC 2 Type II typically incurs higher costs. In this case, the platform fee results in a net saving of . This confirms that while automation reduces headcount dependency, it introduces a fixed cost that must be justified by volume.

| Control Domain | Manual Baseline (Hours) | Automated Result (Hours) | Savings | Mechanism |
| --- | --- | --- | --- | --- |
| Evidence Gathering (CC6.1/CC7.2) | 96 | 52 | 44 | API-driven log aggregation |
| Remediation (CC7.3/CC8.1) | 74 | 38 | 36 | Auto-flagged approval gaps |
| Auditor Q&A | 44 | 38 | 6 | 100% population coverage |
| Total Prep Time | 214 | 128 | 86 | 40.2% reduction |

The outcome—zero exceptions and a report issued in 42 days versus 53 days prior year—highlights the acceleration provided by 100% population coverage. Unlike manual sampling which extrapolates from a subset, automated testing validates every transaction. According to RiskRecon by Mastercard, use cases include Prioritize Critical Risks, Collaborate on Risk Remediation, and Strengthen Supply Chain Resilience, all of which depend on the completeness of the underlying data. When the data is complete, the auditor’s role shifts from validation to review, compressing the timeline by 11 days. This acceleration is not merely a speed gain; it is a risk mitigation strategy that ensures no control failure escapes detection due to sampling error.

Most compliance teams default to automation because it feels like progress, but the 2026 SOC 2 Type II audit landscape rewards precision over volume. The decision to automate API-based continuous testing for high-volume system controls cuts total audit prep time by ~40% versus manual sampling, while judgment-based entity-level controls show no time saving. This divergence is not about technology capability; it is about data structure and auditor acceptance. You must apply a strict decision tree that separates machine-readable evidence from human interpretation.

![From Hours — Cut audit prep time](https://static.mm-ais.com/article-images-pixabay/cut-audit-prep-time-40-system-and-organi-95bc13fb.jpg)

## How to Choose Well

The first filter is event volume. If CC6.2 authentication logs exceed 500 events per month, automate collection; if under 50 events per quarter, keep manual sampling. High-frequency logs drown auditors in noise unless they are aggregated via API. Low-frequency logs are cheap to review manually. The second filter is control cadence and format. If control operates daily or weekly with structured machine log such as CC6.3 encryption status, automate; if quarterly policy attestation, sample manually. Structured logs allow for deterministic validation; policy attestations require reading comprehension that APIs cannot yet provide reliably.

This approach aligns with the finding that faster response alone is not enough to build organizational resilience according to the 2026 study (Everbridge 2026 Best in Resilience Study). Speed without structural integrity creates false confidence. By automating only where the data is clean and the volume is high, you preserve auditor trust and reduce prep time. Defer automation where judgment is required or costs are prohibitive. This selective strategy ensures that your 40% time savings are real, not theoretical.

The third filter is historical risk. If prior-year exceptions equal zero and auditor pre-approved the API source, automate; if 2 or more exceptions last year, retain sampling for one full cycle. Auditors scrutinize automated evidence heavily when past performance was flawed. They need to see the manual work to verify the fix before trusting the code. The fourth filter is implementation cost relative to scale. If connector setup takes under 8 hours and costs under per year and headcount exceeds 100 employees, automate; otherwise defer. For smaller entities, the fixed cost of integration outweighs the variable savings in audit hours. The fifth filter is the nature of the evidence itself. If evidence requires vendor judgment such as CC9.2 vendor risk reviews or penetration-test interpretation, always use manual sampling and never rely solely on automation. Judgment is inherently non-deterministic.

| Control Type | Condition | Action | Rationale |
| --- | --- | --- | --- |
| CC6.2 Auth Logs | >500 events/month | Automate | Volume exceeds manual review capacity |
| CC6.2 Auth Logs |  | Manual Sample | Low volume makes automation inefficient |
| CC6.3 Encryption | Daily/Weekly Machine Log | Automate | Structured data allows API extraction |
| Policy Attestation | Quarterly Human Review | Manual Sample | Requires human judgment and context |
| Risk History | Zero Exceptions + Pre-Approved API | Automate | Auditor trust established |
| Risk History | 2+ Exceptions Last Year | Manual Sample | Need to verify remediation manually |
| Implementation Cost |  | Automate | ROI positive at scale |
| Implementation Cost | Otherwise | Defer | Fixed costs outweigh savings |
| Evidence Type | Vendor Judgment (e.g., CC9.2) | Manual Sample | Judgment cannot be fully automated |

This approach aligns with the finding that faster response alone is not enough to build organizational resilience according to the 2026 study (Everbridge 2026 Best in Resilience Study). Speed without structural integrity creates false confidence. By automating only where the data is clean and the volume is high, you preserve auditor trust and reduce prep time. Defer automation where judgment is required or costs are prohibitive. This selective strategy ensures that your 40% time savings are real, not theoretical.

## What to do next

| Step | Action | Why it matters |
| --- | --- | --- |
| 1 | Automate CC6.1 in Vanta pulling AWS CloudTrail for full-population testing instead of AICPA Audit Sampling Guide ticket sampling | Cuts prep time by 40% while improving defensibility |
| How much can switching high-volume controls to automated pulls cut SOC 2 prep time? | Switching high-volume controls to automated pulls cuts prep time by 40% versus manual samples while improving defensibility. |  |
| Why replace spreadsheets with persistent evidence pipelines? | Manual work relies on spreadsheets and emails; validated pipelines reduce human error and audit fatigue. |  |
| What do persistent pipelines support for SOC 2 assessment? | Persistent pipelines support continuous monitoring for examination, interview, and testing. |  |
| How do automated pulls work for SOC 2 controls? | For SOC 2, automated pulls test every event against the control, while examination, interview, and testing procedures confirm correct implementation and operation. |  |
| How does continuous monitoring help auditors avoid sampling risk? | Continuous monitoring prioritizes threats based on real-world conditions and asset importance, so missed or outdated controls are caught early and auditors get complete, consistent evidence without sampling risk. |  |

Also worth reading: **Audit anomaly detection 2026: Isolation Forest Audit Standard (ISA 315) 30% vs Hold**: [Audit anomaly detection 2026: Isolation](https://financialauditexpert.com/blog/audit-anomaly-detection-2026-isolation-forest-audit-standard-isa-315-30-vs-hold.php) · **Cut audit hours explained: 42 accelerated filers replace sampling test**: [Cut audit hours explained: 42](https://financialauditexpert.com/blog/cut-audit-hours-explained-42-accelerated-filers-replace-sampling-test.php) · **Simplifying the transition to PCI DSS 4.0 with automated audit technology**: [Simplifying the transition to PCI](https://financialauditexpert.com/blog/simplifying-the-transition-to-pci-dss-40-with-automated-audit-technology.php)

### Related reading

- [7 Critical Indicators of IT Audit Maturity Benchmarking Your Organization's 2024 Compliance Framework](https://financialauditexpert.com/blog/7_critical_indicators_of_it_audit_maturity_benchmarking_your.php)
- [Cut audit hours explained: 42 accelerated filers replace sampling test](https://financialauditexpert.com/blog/cut-audit-hours-explained-42-accelerated-filers-replace-sampling-test.php)
- [How to Strengthen Internal Controls and Improve the Success of Your Financial Audit](https://financialauditexpert.com/blog/how-to-strengthen-internal-controls-and-improve-the-success-of-your-financial-audit.php)
- [Deloitte Full-Time Application Deadlines Key Dates and Strategies for Financial Audit Candidates in 2025](https://financialauditexpert.com/blog/deloitte_full_time_application_deadlines_key_dates_and_strat.php)
- [7 Key Financial Metrics That Reveal Your Organization's Cultural Health in 2024](https://financialauditexpert.com/blog/7_key_financial_metrics_that_reveal_your_organization_s_cult.php)
- [PCAOB 2026: Why Threshold Sampling Fails and CDDA Falls Short](https://financialauditexpert.com/blog/pcaob-2026-why-threshold-sampling-fails-and-cdda-falls-short.php)

### Latest

- [Cut audit hours explained: 42 accelerated filers replace sampling test](https://financialauditexpert.com/blog/cut-audit-hours-explained-42-accelerated-filers-replace-sampling-test.php)
- [Financial Services Compliance: Complete 2026 Guide - Xantrion](https://financialauditexpert.com/blog/financial-services-compliance-complete-2026-guide-xantrion.php)
- [Audit anomaly scores explained: 5% flagged means expand testing](https://financialauditexpert.com/blog/audit-anomaly-scores-explained-5-flagged-means-expand-testing.php)

Canonical: https://financialauditexpert.com/blog/cut-audit-prep-time-40-system-and-organization-controls-2-soc-2-auto-vs-sampling-2026.php
Markdown: https://financialauditexpert.com/blog/cut-audit-prep-time-40-system-and-organization-controls-2-soc-2-auto-vs-sampling-2026.php/index.md