Privacy code scanning software that finds the personal data in your code

Choose privacy code scanning software by the evidence it can produce and the review controls around that evidence. A useful scanner should surface personal-data handling, processor SDKs, transfer indicators and AI components with the file, line, branch and commit where each was found, map each finding to the obligation it may trigger (Article 30 RoPA, Article 35 DPIA, Chapter V transfers, EU AI Act follow-up), and let a named reviewer confirm, edit or reject the finding before it becomes part of the compliance record. Acompli Code Scan is built around this evidence-and-review model and is a paid add-on to the Acompli platform; it complements rather than replaces SAST, SBOM and vulnerability tooling.

The best privacy code scanning software is decided less by how many patterns it matches than by whether each finding carries the file, line, branch and commit where it was found, maps to the obligation it may trigger (Article 30 RoPA, Article 35 DPIA, Chapter V transfers, EU AI Act follow-up), and is confirmed by a named reviewer before it becomes evidence. Acompli's angle is to govern these as connected, human-approved records tied to the wider GDPR and EU AI Act programme, each finding traceable to the code that produced it; it complements rather than replaces SAST, SBOM and vulnerability tooling. For a balanced, unbiased comparison of the available suppliers - evidence-based, drawn from public sources, and honest about where competitors are stronger than Acompli - we have compiled capability charts and vendor-by-vendor breakdowns for you to consider in our full comparison library.

A PII-in-code scanner analyses source code for personally identifiable information and the signals around it: where personal data is collected, stored, logged or shared, which processor SDKs are called, and where data crosses borders. Acompli Code Scan reads nominated GitHub repositories through zero-copy access and looks across source, dependencies, database schemas, configuration and infrastructure-as-code together, and every finding carries the file, line, branch and commit where it was found so a reviewer can confirm it before it becomes evidence. It surfaces PII signals for human review and does not make the legal determination itself.

A privacy-focused code scanner looks for personally identifiable information grouped by GDPR relevance: contact identifiers such as names, emails and phone numbers; government identifiers such as PPSN, passport or national ID numbers; financial data such as IBANs and card numbers; online identifiers such as IP addresses, device IDs and cookies; and special-category data under Article 9 such as health or biometric data. Acompli Code Scan flags these across source, dependencies, schemas, configuration, environment files and infrastructure-as-code, and maps each finding to the obligation it triggers: special-category data to an Article 9 and Article 35 DPIA, a transfer indicator to Chapter V and SCCs, a processor SDK to the Article 30 RoPA. These are reviewable signals for a DPO, not automated classifications.

No. Acompli Code Scan is not a vulnerability scanner, SAST tool, malware scanner or SBOM generator. It looks for privacy and AI-governance evidence - what personal data the code handles, which processors it calls, where data crosses borders, which AI components are in use - and routes those findings into RoPA, DPIA and AI Act work. Keep your security tooling; Code Scan answers a different question.

Code-level signals confirm what the Article 30 register claims: which systems actually process personal data, which processor SDKs are embedded, which data categories appear in schemas, and where transfers happen. Approved findings flow into RoPA records with their provenance attached, so the register the DPC or ICO inspects is grounded in what the code does rather than what an interview remembered.

GDPR and the UK GDPR do not require code scanning by name, but they do require accurate records (Article 30 RoPA), DPIAs for high-risk processing (Article 35) and a defensible account of transfers (Chapter V) - and both the DPC in Ireland and the ICO in the UK expect those records to reflect what systems actually do. Privacy code scanning helps teams ground that evidence in the codebase rather than in interviews and spreadsheets. Acompli Code Scan surfaces personal-data handling, processor SDKs, transfer indicators and AI components with file-and-commit provenance, and a named reviewer approves each finding before it becomes part of a RoPA or DPIA the DPC or ICO might inspect.

A DPIA is only as good as its description of the processing. Code Scan provides the technical half: evidence of data collection points, sharing and transfer indicators, retention-relevant logic and AI components, each finding carrying file-and-commit provenance. Reviewers attach approved findings to the Article 35 assessment, so necessity and proportionality are argued from the actual implementation.

No. Code Scan surfaces reviewable signals - it never patches code, opens pull requests or makes a legal determination. A human confirms, rejects or annotates every finding before it becomes part of the compliance record, and the EU AI Act risk-tier hints it attaches are inputs for human classification, not classifications.

No — it isn't an isolated scan report. Code Scan is a paid add-on to the Acompli platform: findings are reviewed with provenance and flow into your assessments, risk, records, and AI-governance work, so privacy, legal, security, and engineering teams act on the same evidence rather than a one-off export.

Code Scan is a paid add-on to any Acompli platform plan, priced separately from the five-module platform rather than bundled into every licence. Request pricing based on repository scope, review workflow and security requirements.

The scan starts with governance purpose, not technical curiosity. Teams frame the exercise around privacy review, AI-governance review, or both together, then align the scan to the repository context and the actual compliance question before results are generated.

Code Scan is privacy code scanning rather than a narrow keyword scan. It helps teams identify evidence relevant to personal-data handling, vendor and processor signals, transfer follow-up, and AI-related components by looking across source code, dependencies, schemas, configuration, and implementation patterns together.

Findings arrive with provenance so reviewers can understand what was found, where it was found, and why it matters. Human reviewers can confirm, reject, annotate, and refine findings before anything is treated as an official output, giving privacy and engineering teams a shared review layer rather than a one-sided alert queue.

Code Scan can support both within the same operating model. Privacy review can focus on personal-data handling, processors, and transfer signals, while AI-governance review can focus on model integrations, SDK usage, and other components that may need internal review or EU AI Act follow-up. The value is that both lenses can work from the same technical evidence.

Code Scan surfaces AI-related components, model integrations and SDK usage as reviewable signals that flag systems for follow-up — it does not decide whether a system is high-risk. The Annex III classification lives on the EU AI Actpage, and the mechanics of how approved findings feed an Article 35 DPIA and Article 30 RoPA live on the Assessments page. Code Scan provides the human-reviewed technical evidence those determinations rely on.

Yes. Alongside personal-data signals, Code Scan detects the AI in your code — AI and machine-learning SDKs, model integrations and general-purpose AI (GPAI) API calls — so an AI system inventory is grounded in what the code actually imports rather than a self-reported survey. Each finding carries the file, line, branch and commit where it was found and is flagged for EU AI Act follow-up: Annex III high-risk patterns, Article 50 transparency scenarios and the AI system register. Code Scan surfaces these as reviewable signals with a provisional risk-tier hint; a person makes the high-risk determination, and the Annex III classification itself lives on the EU AI Act page. It is the same scan read through the AI-governance lens rather than only the privacy one.