Automated privacy checks have streamlined audits and free my engineers for faster innovationPrivado offers multiple great features that I consider personal favorites, such as continuous code-level scanning and data flow mapping, which have helped me understand how personal data is ingested, processed, stored, and transmitted directly from the source code, eliminating guesswork. Additionally, it has facilitated shift-left privacy engineering by providing my developers with actionable feedback directly within pull requests, allowing privacy checks similar to static security scans like SAST. It has automated my evidence collection, drastically reducing audit preparation cycles for compliance reviews and privacy impact assessments. I also appreciate how it unifies the bridge between legal and engineering teams by translating complex regulatory requirements into concrete code checks, effectively ending friction between the teams and developers. Regarding Privado's AI capabilities, I find its governance and security to be an excellent addition to standard data privacy compliance. It features proven AI governance and data deployment models that help scan codebases and pipelines to ensure that proprietary code, sensitive user PII, and restricted datasets do not get routed into LLMs. Additionally, it ensures that embedded AI agents track libraries and interface APIs across all services, providing me with full lineage and governance over AI inputs and outputs at the code level. The accuracy and reliability of Privado's AI output is solid. I would give it a 10 out of 10 since it provides the right answers with the correct context, and I have not encountered AI hallucinations when posing challenging questions.
