<?xml version="1.0" encoding="UTF-8"?><rss xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:content="http://purl.org/rss/1.0/modules/content/" xmlns:atom="http://www.w3.org/2005/Atom" version="2.0"><channel><title><![CDATA[Maya Data Privacy]]></title><description><![CDATA[Maya Data Privacy]]></description><link>https://www.mayadataprivacy.com/blog</link><generator>RSS for Node</generator><lastBuildDate>Wed, 11 Nov 2026 22:37:06 GMT</lastBuildDate><atom:link href="https://www.mayadataprivacy.com/de/blog-feed.xml" rel="self" type="application/rss+xml"/><item><title><![CDATA[PII-Erkennung in SAP: Warum unsere spezialisierte KI ein Foundation Model schlägt]]></title><description><![CDATA[Maya PIIDetect findet 91,2 % der personenbezogenen Felder in SAP, SAP RPT-1 OSS 66,4 %, bei 34-mal schnellerer Inferenz auf CPU. Der Benchmark.]]></description><link>https://www.mayadataprivacy.com/de/post/pii-erkennung-sap-benchmark</link><guid isPermaLink="false">6af1cd21e2d171a86cd8965b</guid><pubDate>Mon, 09 Nov 2026 16:44:10 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_bc22c95b40e14d73a1928efc8157d3a4~mv2.jpg/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[SAP-Testdaten anonymisieren und die referenzielle Integrität erhalten]]></title><description><![CDATA[Viele SAP-Testsysteme laufen mit echten Produktionsdaten. So anonymisieren Sie SAP-Testdaten DSGVO-konform und systemübergreifend konsistent.]]></description><link>https://www.mayadataprivacy.com/de/post/sap-testdaten-anonymisieren-referenzielle-integritaet</link><guid isPermaLink="false">6af1cd20512fb9710c6ef2c1</guid><pubDate>Mon, 09 Nov 2026 16:43:50 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_d84e7e35287f412cb911eb731be8040c~mv2.png/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[ChatGPT im Unternehmen: So bleiben sensible Daten geschützt]]></title><description><![CDATA[So verhindern Sie, dass sensible Daten in ChatGPT und Unternehmens-LLMs gelangen: fünf praktische Schritte bis hin zum Privacy Gateway.]]></description><link>https://www.mayadataprivacy.com/de/post/chatgpt-datenschutz-unternehmen</link><guid isPermaLink="false">6af1cd8b314c3a7816c44b59</guid><pubDate>Mon, 09 Nov 2026 14:00:03 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_a6a3f063dd6749fea4721832ffe60857~mv2.jpg/v1/fit/w_1000,h_346,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Abhinava</dc:creator></item><item><title><![CDATA[Anonymisierte oder synthetische Daten: Was funktioniert für KI im Unternehmen?]]></title><description><![CDATA[Anonymisierte und synthetische Daten für KI-Training im Vergleich: Welcher Ansatz erhält Datenqualität, Systemkomplexität und Rechtssicherheit?]]></description><link>https://www.mayadataprivacy.com/de/post/anonymisierte-vs-synthetische-daten-ki</link><guid isPermaLink="false">6af1cdee8393d090b40d23a7</guid><pubDate>Mon, 09 Nov 2026 13:59:40 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_f4826e2b60204630b4fed66d7beb5121~mv2.png/v1/fit/w_1000,h_562,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[KI-Tools und SAP-Daten: Warum Verträge nicht reichen]]></title><description><![CDATA[VISCHERs Compliance-Übersicht 2026 zeigt: ChatGPT, Claude und Copilot dürfen sensible SAP-Daten nicht verarbeiten. Die technische Lösung.]]></description><link>https://www.mayadataprivacy.com/de/post/ki-tools-sap-daten-datenschutz</link><guid isPermaLink="false">6af1cdef65e66e97baa94cf9</guid><pubDate>Mon, 09 Nov 2026 13:59:17 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_c020133db5fd4dd4bdeadcc11aa18652~mv2.png/v1/fit/w_1000,h_768,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[DSGVO-Bußgelder 2024/25: Die 10 höchsten Strafen und wie Anonymisierung sie verhindert hätte]]></title><description><![CDATA[1,77 Milliarden Euro DSGVO-Bußgelder, neun von zehn Fällen vermeidbar: die höchsten Strafen 2024/25 und wie Anonymisierung sie verhindert.]]></description><link>https://www.mayadataprivacy.com/de/post/dsgvo-bussgelder-2024-2025-anonymisierung</link><guid isPermaLink="false">6af1cd89cf86ae121bbf5ca7</guid><pubDate>Mon, 09 Nov 2026 13:58:34 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_8c8df8796eb6458587e4eded50d5251b~mv2.jpg/v1/fit/w_1000,h_414,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[DEPA für KI-Training: Erst anonymisieren, dann trainieren]]></title><description><![CDATA[Indiens DEPA für KI-Training verlangt, dass Daten an der Quelle anonymisiert werden, bevor sie geteilt werden. Was das für Dateneigentümer heißt.]]></description><link>https://www.mayadataprivacy.com/de/post/depa-ki-training-anonymisierung</link><guid isPermaLink="false">6af1bc51bc489f660e58654b</guid><pubDate>Mon, 09 Nov 2026 12:29:33 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_512932b358e141ad89bfed436d152a00~mv2.png/v1/fit/w_1000,h_768,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[DEPA for AI Training: why anonymization comes first]]></title><description><![CDATA[India's DEPA for AI Training puts anonymization at the data source before any sharing. What that means for data owners, and where Maya fits.]]></description><link>https://www.mayadataprivacy.com/post/depa-for-ai-training-anonymization-first</link><guid isPermaLink="false">6aeafae346d2cd453a8cbbd3</guid><pubDate>Sat, 07 Nov 2026 09:47:51 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_bd1f6dcc6df7443dadf870bda43f988e~mv2.png/v1/fit/w_1000,h_768,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[Data anonymisation: shrinking India's breach exposure]]></title><description><![CDATA[SBI, Tata Electronics, Bank of Baroda: three breaches in 2026. Where personal data really sits, and how anonymisation cuts exposure before DPDP enforcement.]]></description><link>https://www.mayadataprivacy.com/post/data-anonymisation-india-breach-exposure</link><guid isPermaLink="false">6adb05ba7a16f6a3ff83c3b1</guid><pubDate>Fri, 23 Oct 2026 07:55:36 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_1c2f3d7b5d0c4fd1828f3fb0de5fd12f~mv2.png/v1/fit/w_1000,h_890,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[GLiNER Can Now Detect Names and PII in Indian Languages, in Their Own Scripts]]></title><link>https://www.mayadataprivacy.com/post/gliner-can-now-detect-names-and-pii-in-indian-languages-in-their-own-scripts</link><guid isPermaLink="false">6ada3a5a799cb52c764686aa</guid><pubDate>Thu, 22 Oct 2026 16:33:52 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e55667_75b6855082cc480c9e11b3227b0725a0~mv2.png/v1/fit/w_1000,h_887,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>contractorweb</dc:creator></item><item><title><![CDATA[Your AI Tools Cannot Legally Touch Your SAP Data]]></title><description><![CDATA[Switzerland's leading AI law firm just mapped every major enterprise AI tool against GDPR and DSG. The verdict: sensitive personal data in SAP systems has no clean contractual path to any of them. We explain why anonymization at source is the fix the legal route can't provide.]]></description><link>https://www.mayadataprivacy.com/post/ai-tools-sap-data-protection-compliance</link><guid isPermaLink="false">6a670e5b388acffd1e3a2d97</guid><pubDate>Mon, 27 Jul 2026 08:03:06 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_c020133db5fd4dd4bdeadcc11aa18652~mv2.png/v1/fit/w_1000,h_768,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[Why We Built Our Own AI for SAP PII Detection and Beat a Foundation Model]]></title><description><![CDATA[Our purpose-built PIIDetect catches 91.2% of PII in SAP vs. 66.4% for SAP RPT-1 OSS and runs 34× faster on CPU. The benchmark, and why specialization wins.]]></description><link>https://www.mayadataprivacy.com/post/pii-detection-sap-benchmark-piidetect-vs-rpt1</link><guid isPermaLink="false">6a204018c9d8b2a0564edfbe</guid><pubDate>Wed, 03 Jun 2026 16:51:49 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_e6559a6b0523445696f646c4a029f058~mv2.jpg/v1/fit/w_848,h_630,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[This is how end-to-end application data anonymization works in SAP]]></title><description><![CDATA[SAP landscapes contain highly sensitive personal data: customer master data, employee information, contract data, and payment information. As soon as this production data is copied into test, training, or development environments, a massive GDPR risk arises. Article 25 of the GDPR requires privacy by design. However, many companies fail to implement end-to-end application data anonymization in SAP landscapes. The consequence: Personal data in unprotected non-production systems. Access by...]]></description><link>https://www.mayadataprivacy.com/post/this-is-how-end-to-end-application-data-anonymization-works-in-sap</link><guid isPermaLink="false">69ef5eb96a964ded953addb9</guid><pubDate>Mon, 27 Apr 2026 13:27:54 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_90efd51161e8432b917c94fd98fd1375~mv2.png/v1/fit/w_1000,h_630,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[So funktioniert durchgängige Anwendungsdaten-Anonymisierung in SAP]]></title><description><![CDATA[SAP-Landschaften enthalten hochsensible Personendaten: Kundenstammdaten, Mitarbeiterinformationen, Vertragsdaten, Zahlungsinformationen. Sobald diese Produktionsdaten in Test-, Schulungs- oder Entwicklungsumgebungen kopiert werden, entsteht ein massives DSGVO-Risiko. Artikel 25 DSGVO fordert Privacy by Design. Doch viele Unternehmen scheitern an der technischen Umsetzung durchgängiger Anwendungsdaten-Anonymisierung in SAP-Landschaften. Die Folge: Personenbezogene Daten in ungeschützten...]]></description><link>https://www.mayadataprivacy.com/de/post/anwendungsdaten-anonymisierung-sap</link><guid isPermaLink="false">69ef55807cb0726b2da74f3f</guid><pubDate>Mon, 27 Apr 2026 13:01:42 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_90efd51161e8432b917c94fd98fd1375~mv2.png/v1/fit/w_1000,h_630,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[Production Data Copy Across Complex Enterprise Landscapes]]></title><description><![CDATA[How MAYA AppSafe delivers AI-powered data protection across SAP RISE, on-premises, hyperscaler, and air-gapped environments:with full sovereignty and zero compromises. The Challenge: Complex Landscapes, Consistent Privacy and anonymity Modern SAP landscapes are no longer monolithic. Organizations run S/4HANA on SAP RISE, maintain legacy ECC systems on-premises, deploy applications on hyperscalers, and increasingly face regulatory constraints dictating where data can reside. For data privacy...]]></description><link>https://www.mayadataprivacy.com/post/ai-data-protection-sap-rise-appsafe</link><guid isPermaLink="false">69bada05c22e90b51af3a8cd</guid><pubDate>Wed, 18 Mar 2026 17:20:06 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/888e4e_e52ad97eebf14ddc933109efb0c771c1~mv2.png/v1/fit/w_960,h_540,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Aaloka Anant</dc:creator></item><item><title><![CDATA[Data Privacy for German Financial Services - BaFin &#38; DORA Compliance]]></title><description><![CDATA[German banks, insurance companies, and financial institutions face some of the strictest data protection requirements in Europe. In addition to GDPR (DSGVO) and BDSG, BaFin-regulated entities must comply with DORA (Digital Operational Resilience Act) and MaRisk requirements for IT risk management.  Maya Data Privacy provides AI-driven anonymization tools that help financial institutions meet these requirements at the data layer. Regulatory Landscape for German Financial Services GDPR (DSGVO)...]]></description><link>https://www.mayadataprivacy.com/post/data-privacy-for-german-financial-services-bafin-dora-compliance</link><guid isPermaLink="false">69c029140160b7de31517fbf</guid><pubDate>Sat, 07 Mar 2026 18:30:00 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_b1135946f4094fa787c6665713313002~mv2.png/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[Privacy Isn't the Bottleneck]]></title><description><![CDATA[Why enterprises blame GDPR for an engineering problem they built themselves, and how privacy-ready data architecture actually accelerates AI Key Takeaways: The primary blocker to enterprise AI adoption is poor data architecture, not GDPR or the EU AI Act. Organisations with mature data governance report significantly fewer compliance-related delays in AI projects. Privacy-ready data architecture requires three capabilities: discovery, lineage, and automated classification. Privacy-Enhancing...]]></description><link>https://www.mayadataprivacy.com/post/privacy-isnt-the-bottleneck-gdpr-ai-data-architecture</link><guid isPermaLink="false">69abec9e95f896866359ca18</guid><pubDate>Sat, 07 Mar 2026 10:01:41 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_75581ce46bc24cc78a66199391925603~mv2.jpg/v1/fit/w_1000,h_827,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[Synthetic Test Data for German Companies]]></title><description><![CDATA[German enterprises running SAP, Oracle, and other enterprise systems need realistic test data for development, QA, and AI/ML training. Using real production data in non-production environments creates significant risk under GDPR (DSGVO) and BDSG. Maya Data Privacy solves this by creating fully operational, anonymized copies of production databases that are statistically valid but contain zero real personal data. The Problem Development and QA teams need realistic data to test properly Copying...]]></description><link>https://www.mayadataprivacy.com/post/synthetic-test-data-for-german-companies</link><guid isPermaLink="false">69c027ba0160b7de31517c06</guid><pubDate>Wed, 04 Mar 2026 18:30:00 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_8665b2e9e75d4944baf5670c5daf7fcb~mv2.jpg/v1/fit/w_1000,h_1000,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ranbir</dc:creator></item><item><title><![CDATA[GDPR (DSGVO) and BDSG Compliance Solutions for German Enterprises]]></title><description><![CDATA[German enterprises face a dual compliance challenge: the EU General Data Protection Regulation (GDPR / DSGVO) and the German Federal Data Protection Act (Bundesdatenschutzgesetz / BDSG). Both require organisations to protect personal data through appropriate technical and organisational measures. Maya Data Privacy, headquartered in Dublin, Ireland, provides AI-driven anonymization tools that help German organisations meet these requirements at the data layer. The Compliance Challenge GDPR...]]></description><link>https://www.mayadataprivacy.com/post/gdpr-dsgvo-and-bdsg-compliance-solutions-for-german-enterprises</link><guid isPermaLink="false">69c025e86e2d8139248fdec8</guid><pubDate>Mon, 02 Mar 2026 18:30:00 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e55667_63d66e91fed648dcb505222a5370b53f~mv2.png/v1/fit/w_1000,h_941,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item><item><title><![CDATA[GDPR Fines 2024–2025: The 10 Most Expensive Penalties and How Anonymization Would Have Prevented Most of Them]]></title><description><![CDATA[€1.77B in GDPR fines across 10 companies — and 9 out of 10 were preventable. We break down the largest GDPR penalties of 2024–2025 and show how data anonymization would have stopped most of them.]]></description><link>https://www.mayadataprivacy.com/post/gdpr-fines-2024-2025-top-10-penalties-anonymization</link><guid isPermaLink="false">69a47a622b1318a2484f9f24</guid><pubDate>Sun, 01 Mar 2026 19:36:28 GMT</pubDate><enclosure url="https://static.wixstatic.com/media/e70330_8c8df8796eb6458587e4eded50d5251b~mv2.jpg/v1/fit/w_1000,h_414,al_c,q_80/file.png" length="0" type="image/png"/><dc:creator>Ben Ramhofer</dc:creator></item></channel></rss>