How Quttera’s Evidence-as-Code API Is Reinventing SOC 2 and PCI DSS v4.0 Compliance
Backed by an AI-powered Threat Encyclopedia, this new approach treats compliance artifacts as machine-readable data, enabling continuous audit readiness, streamlined reporting for auditors, and tighter integration between security operations and governance. In this article, we break down Quttera’s mission, core technology, scientific and engineering underpinnings, early milestones, potential challenges, and what this shift toward Evidence-as-Code means for the future of cloud security and regulated industries.
Tel Aviv–based cybersecurity firm Quttera has announced a new Evidence-as-Code API designed to automate security compliance for SOC 2 and PCI DSS v4.0. Launched on November 27, 2025 via CyberNewsWire and covered by outlets like NextBigFuture, the platform promises to replace ad hoc screenshots, emails, and spreadsheets with real-time, verifiable evidence streams that can be consumed by auditors and governance platforms.
At its core, the solution combines Quttera’s existing malware and threat detection capabilities with a new AI-powered Threat Encyclopedia, enabling organizations to map live security telemetry directly to formal compliance controls. This is part of a broader industry movement toward compliance-as-code and security-as-code, where governance is encoded, automated, and continuously enforced in software rather than handled as a periodic, manual exercise.
According to early statements, Quttera’s Evidence-as-Code API aims to make organizations “continuously audit-ready,” sharply reducing the time and cost required to maintain certifications while improving the quality and timeliness of security controls.
“Continuous compliance is no longer a luxury for digital businesses; it is a baseline requirement. Evidence-as-Code closes the gap between what is happening in your environment and what auditors need to see.”
— Adapted from current thinking by leading GRC and DevSecOps practitioners
Mission Overview
The mission behind Quttera’s Evidence-as-Code API is straightforward but ambitious: turn security compliance from an episodic, manual process into an automated, real-time capability. In practice, this means:
- Eliminating manual evidence gathering for SOC 2 and PCI DSS v4.0 audits.
- Producing standardized, machine-readable evidence that can be programmatically verified.
- Linking operational security events (e.g., malware detections, configuration changes) directly to compliance controls.
- Providing auditors with real-time or near real-time visibility into control effectiveness.
- Reducing human error and subjective interpretation across audit cycles.
These objectives align with a broader industry trend: the shift from point-in-time certification to continuous compliance. Frameworks like Cloud Controls Matrix (CCM) and NIST’s risk management guidance already emphasize ongoing monitoring. Quttera’s Evidence-as-Code model attempts to operationalize that principle specifically for SOC 2 and PCI DSS v4.0, two of the most widely adopted standards for cloud services and payment environments.
Technology: Inside Quttera’s Evidence-as-Code and AI Threat Encyclopedia
The technical innovation in Quttera’s launch is twofold: the Evidence-as-Code API itself, and the AI-powered Threat Encyclopedia that contextualizes security events against formal compliance controls.
Evidence-as-Code Architecture
Quttera’s Evidence-as-Code approach treats every relevant security or configuration artifact as a versioned, queryable data object. Instead of static PDFs or screenshots, auditors and GRC systems consume:
- Structured evidence documents (e.g., JSON) describing events, configurations, and states.
- Cryptographic integrity checks (e.g., hashes, time-stamped logs) to verify authenticity.
- Control mappings that connect each evidence item to specific SOC 2 or PCI DSS v4.0 requirements.
- APIs and webhooks that enable continuous data ingestion into governance dashboards.
From a software engineering perspective, this resembles:
- Infrastructure-as-Code (IaC), where cloud infrastructure is declared and version-controlled.
- Compliance-as-Code, where policies and controls are encoded as machine-readable rules.
- Observability pipelines, which stream telemetry from runtime systems into analytics tools.
AI-Powered Threat Encyclopedia
The new Threat Encyclopedia is an AI-augmented knowledge base that aggregates:
- Threat intelligence feeds (malware signatures, exploit patterns, TTPs).
- Historical incident data from Quttera’s telemetry.
- Public vulnerability data (NVD, CVE lists).
- Control requirements from SOC 2 and PCI DSS v4.0.
Using machine learning and rule-based reasoning, the system:
- Classifies security events and artifacts into risk categories.
- Maps them to compliance controls (e.g., SOC 2 CC6.1, PCI DSS v4.0 6.3.3).
- Generates human-readable rationales that explain why certain evidence satisfies (or fails) a requirement.
“The next step for compliance automation is not just collecting data, but understanding it in the context of risk and regulation.”
— Paraphrased from current AI security research trends (e.g., work presented at IEEE S&P and NDSS)
Integration with Existing Toolchains
While full integration details are still emerging, Evidence-as-Code APIs are typically designed to work with:
- SIEM and XDR platforms (e.g., Splunk, Microsoft Sentinel, CrowdStrike) via log forwarding and APIs.
- CI/CD pipelines (GitHub Actions, GitLab CI, Jenkins) for pre-deployment compliance checks.
- GRC platforms such as Drata, Vanta, or Secureframe for automated control tracking.
Early adopters often pair such solutions with strong observability and cloud posture management tools, such as:
- Practical Cloud Security: A Guide for Secure Design and Deployment – a widely used reference in US security engineering teams.
Scientific and Technical Significance
While Quttera’s announcement is primarily an engineering and product milestone, it is underpinned by several important scientific and technical themes in cybersecurity and software assurance.
From Static Evidence to Real-Time Security Posture
Traditional audits rely on static snapshots. This creates two major problems:
- Staleness – Evidence might be days or weeks out of date.
- Sampling bias – Auditors see only a narrow slice of operational behavior.
Continuous, API-driven evidence streams provide:
- Temporal granularity: More frequent or continuous sampling of controls.
- Broader coverage: Evidence from multiple layers—network, endpoint, application, and identity.
- Statistical robustness: Better basis for assessing control reliability over time.
Formalizing Compliance Mappings
Turning evidence into code requires formal models of both:
- The systems being measured (e.g., servers, containers, identities, code artifacts).
- The regulatory and standards frameworks (e.g., SOC 2’s Common Criteria, PCI DSS v4.0 requirements).
This is closely related to research in:
- Policy as code (e.g., Open Policy Agent, Rego).
- Formal verification of system properties and configurations.
- Knowledge representation for regulatory text and security ontologies.
Security expert Bruce Schneier has long argued that security must be treated as an engineering discipline, not mere checkbox compliance. Evidence-as-Code operationalizes that philosophy by binding compliance directly to measurable system behavior.
— Inspired by Bruce Schneier’s public writings and talks (schneier.com)
Reducing Cognitive Load for Humans
Compliance engineering is cognitively demanding: practitioners must translate dense regulatory language into operational checks. AI-based systems like Quttera’s Threat Encyclopedia can:
- Highlight the most relevant evidence per control.
- Explain mappings between threats and requirements in natural language.
- Surface anomalies or gaps that might indicate control failures.
Key Milestones and Adoption Path
While the November 27, 2025 announcement is an early-phase milestone, it fits into a multi-year trajectory of gradual automation in security compliance.
Industry Milestones Leading to Evidence-as-Code
- 2010s – Spread of DevOps and Infrastructure-as-Code in mainstream cloud environments.
- Late 2010s–early 2020s – Emergence of compliance automation platforms (e.g., continuous SOC 2 evidence collection).
- 2020–2024 – Growth of AI-driven security analytics, threat intelligence enrichment, and security graph models.
- 2024–2025 – Early Evidence-as-Code initiatives integrating GRC, SIEM, and IaC pipelines.
Quttera’s launch marks one of the first public, named Evidence-as-Code APIs focused explicitly on SOC 2 and PCI DSS v4.0, with a built-in AI knowledge base for threat mapping.
Typical Enterprise Adoption Phases
Organizations adopting such technology usually progress through:
- Discovery & Assessment – Evaluate current evidence workflows and tooling gaps.
- Pilot Integration – Connect Evidence-as-Code APIs to a subset of systems (e.g., a PCI in-scope environment).
- Control Mapping – Validate that generated evidence satisfies auditor expectations for specific controls.
- Scale-Out – Extend integration to additional business units and cloud accounts.
- Continuous Optimization – Refine mappings, improve coverage, and add more data sources.
Collaboration with auditors is crucial in these phases to ensure that automated evidence is trusted, interpretable, and acceptable during certification reviews.
Challenges and Limitations
Despite its promise, an Evidence-as-Code model faces technical, organizational, and regulatory challenges.
Trust and Verifiability
Auditors must trust that:
- Evidence has not been tampered with or selectively filtered.
- The mappings from evidence to controls are accurate and complete.
- AI components are explainable enough to justify findings.
This often requires:
- Cryptographic signing of logs and evidence documents.
- Clear data lineage and provenance records.
- Human oversight for high-impact control decisions.
Data Privacy and Scope Creep
Automated evidence collection can inadvertently sweep in:
- Sensitive customer information.
- Employee data or secrets (e.g., API keys, credentials).
Engineering teams must:
- Define strict scoping rules for what evidence is collected.
- Implement redaction and anonymization where necessary.
- Ensure compliance with data protection laws (e.g., GDPR, CCPA).
Cultural and Process Change
Evidence-as-Code affects workflows across:
- Security Operations Centers (SOCs).
- DevOps / Platform engineering teams.
- GRC and internal audit groups.
Success depends on cross-functional agreements about:
- What “good evidence” looks like.
- How much automation is appropriate for critical decisions.
- How exceptions and overrides are handled.
Practical Implications for Security and Compliance Teams
For practitioners responsible for SOC 2 and PCI DSS v4.0, Quttera’s Evidence-as-Code API offers several concrete benefits and considerations.
Potential Benefits
- Reduced audit preparation time – Less time collecting screenshots, exports, and ad hoc reports.
- Higher evidence quality – Consistent, structured documentation of control behavior.
- Continuous visibility – GRC dashboards that reflect near-real-time posture rather than static reports.
- Faster remediation cycles – Automated detection of evidence gaps and control failures.
Implementation Considerations
Before integrating such an API, teams should:
- Inventory current evidence sources (logs, reports, configurations).
- Identify high-friction controls that are hardest to evidence today.
- Work with Quttera (or similar vendors) to map controls to data sources.
- Run a parallel pilot where automated and manual evidence are both collected for comparison.
Supporting Learning Resources
To strengthen foundational knowledge in this area, many teams rely on resources like:
- SOC 2 for Startups: A Guide for Engineers and Founders – for understanding SOC 2 from an engineering perspective.
- PCI DSS: A Practical Guide to Implementing and Maintaining Compliance – to interpret PCI DSS requirements in operational environments.
Conclusion
Quttera’s launch of an Evidence-as-Code API and an AI-powered Threat Encyclopedia represents a significant step in the evolution of automated compliance. By converting audit evidence into structured, real-time data streams, the company aims to reduce manual effort, increase transparency, and tightly couple security operations with formal governance frameworks like SOC 2 and PCI DSS v4.0.
The approach is not without challenges—trust, explainability, and privacy must be carefully managed—but it aligns strongly with the direction of modern DevSecOps and cloud-native security. As regulatory expectations grow and environments become more dynamic, the organizations that thrive will be those that treat compliance not as an afterthought, but as an integrated, code-driven capability.
“Compliance is moving from paperwork to real-time telemetry. Evidence-as-Code is the bridge between the two.”
— Contemporary view among security and GRC leaders in cloud-native enterprises
Additional Value: How to Prepare Your Organization for Evidence-as-Code
Organizations considering Quttera’s solution—or any Evidence-as-Code platform—can start preparing now with a few concrete steps.
1. Standardize Your Control Library
Build or adopt a central control catalog that maps SOC 2, PCI DSS v4.0, and other frameworks to:
- Specific systems and data flows.
- Existing logs, metrics, and configuration sources.
- Owners and escalation paths.
2. Invest in High-Quality Telemetry
Evidence-as-Code is only as good as the telemetry it can ingest. Prioritize:
- Unified logging standards across services.
- Consistent tags and metadata (e.g., environment, system, owner).
- Robust identity and access management data (for SOC 2 and PCI controls).
3. Establish Auditor Collaboration Early
Engage your external auditors or QSA (Qualified Security Assessor) early to:
- Validate automated evidence formats.
- Align on acceptable sampling strategies.
- Define clear criteria for when manual corroboration is still required.
For ongoing perspective on how automated compliance is evolving, you can follow professional discussions and analysis on platforms like:
- LinkedIn security and GRC communities – where practitioners share patterns and lessons learned.
- YouTube talks on continuous compliance and DevSecOps – for conference talks and case studies.
References / Sources
Selected references and further reading:
- Quttera press coverage and announcement (via NextBigFuture, CyberNewsWire) – publicly accessible as of late November 2025.
- American Institute of CPAs (AICPA) – SOC 2 overview: https://www.aicpa.org/interestareas/frc/assuranceadvisoryservices/soc2.html
- PCI Security Standards Council – PCI DSS v4.0 resources: https://www.pcisecuritystandards.org/pci_security/pci_dss
- NIST – Risk Management Framework and continuous monitoring: https://csrc.nist.gov/publications/detail/sp/800-137/final
- Open Policy Agent – Policy-as-code for cloud-native environments: https://www.openpolicyagent.org/
- Bruce Schneier – Security engineering and policy commentary: https://www.schneier.com/
