Libraries tagged by genitive

Audit trail for Yii3 applications: who changed what and when, with sensitive value masking

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# Introduction **INDA (INtelligent Data Analysis)** is an [Intervieweb](https://www.intervieweb.it/hrm/) AI solution provided as a RESTful API. The INDA pricing model is *credits-based*, which means that a certain number of credits is associated to each API request. Hence, users have to purchase a certain amount of credits (established according to their needs) which will be reduced at each API call. INDA accepts and processes a user's request only if their credits quota is grater than - or, at least, equal to - the number of credits required by that request. To obtain further details on the pricing, please visit our [site](https://inda.ai) or contact us. INDA HR embraces a wide range of functionalities to manage the main elements of a recruitment process: + [**candidate**](https://api.inda.ai/hr/docs/v2/#tag/Resume-Management) (hereafter also referred to as **resume** or **applicant**), or rather a person looking for a job; + [**job advertisement**](https://api.inda.ai/hr/docs/v2/#tag/JobAd-Management) (hereafter also referred to as **job ad**), which is a document that collects all the main information and details about a job vacancy; + [**application**](https://api.inda.ai/hr/docs/v2/#tag/Application-Management), that binds candidates to job ads; it is generated whenever a candidate applies for a job. Each of them has a specific set of methods that grants users the ability to create, read, update and delete the relative documents, plus some special features based on AI approaches (such as *document parsing* or *semantic search*). They can be explored in their respective sections. Data about the listed document types can be enriched by connecting them to other INDA supported entities, such as [**companies**](https://api.inda.ai/hr/docs/v2/#tag/Company-Management) and [**universities**](https://api.inda.ai/hr/docs/v2/#tag/Universities), so that recruiters may get a better and more detailed idea on the candidates' experiences and acquired skills. All the functionalities mentioned above are meant to help recruiters during the talent acquisition process, by exploiting the power of AI systems. Among the advantages a recruiter has by using this kind of systems, tackling the bias problem is surely one of the most relevant. Bias in recruitment is a serious issue that affect both recruiters and candidates, since it may cause wrong hiring decisions. As we care a lot about this problem, we are constantly working on reduce the bias in original data so that INDA results may be as fair as possible. As of now, in order to tackle the bias issue, INDA automatically ignores specific fields (such as name, gender, age and nationality) during the initial processing of each candidate data. Furthermore, we decided to let users collect data of various types, including personal or sensitive details, but we do not allow their usage if it is different from statistical purposes; our aim is to discourage recruiters from focusing on candidates' personal information, and to put their attention on the candidate's skills and abilities. We want to help recruiters to prevent any kind of bias while searching for the most valuable candidates they really need. The following documentation is addressed both to developers, in order to provide all technical details for INDA integration, and to managers, to guide them in the exploration of the implementation possibilities. The host of the API is [https://api.inda.ai/hr/v2/](https://api.inda.ai/hr/v2/). We recommend to check the API version and build (displayed near the documentation title). You can contact us at [email protected] in case of problems, suggestions, or particular needs. The search panel on the left can be used to navigate through the documentation and provides an overview of the API structure. On the right, you can find (*i*) the url of the method, (*ii*) an example of request body (if present), and (*iii*) an example of response for each response code. Finally, in the central section of each API method, you can find (*i*) a general description of the purpose of the method, (*ii*) details on parameters and request body schema (if present), and (*iii*) details on response schema, error models, and error codes.

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Create and format PHP exceptions easily. Automatically unravel method arguments. Ensure that sensitive strings like passwords, tokens, PHPSESSID, etc. are being masked and thus will instead appear as e.g. "******" in the resulting text.

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Cast any PHP value to a sensible, human readable string. Great for type-safe outputs, exception messages, transparency during debugging, and similar things. Also helps avoiding innate problems such as printing endless, circular referencing objects (endless recursion), limits the output for large arrays and long strings, and prevents (opt-in) the outputting of sensitive strings like passwords.

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AI Guardrails Prompt Safety recipe for the Drupal AI module. Installs guardrails to block injection attacks, XSS patterns, and liability-triggering topics such as legal advice, medical advice, and sensitive subjects.

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Prevent sensitive exception details from leaking in Laravel applications

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Pure-PHP PII protection: field-level encryption at rest + masking of sensitive fields in audit/log payloads. No framework, no global state — plain classes with explicit inputs and outputs, usable anywhere.

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Removes sensitive data from payloads before they are logged or persisted: masking by key name, per-request secrets, card number detection.

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Webman 敏感词检测，过滤，标记

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Monolog processor to protect sensitive information from logging by hashing the values

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A Laravel package that provides functionalities for detecting sensitive information and patterns in the database, helping to ensure data privacy and security by empowering developers to easily integrate database scanning capabilities into their applications and take proactive measures to protect sensitive data.

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Sensitive data detection and masking for Symfony applications.

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Sensitive Data Masker for logs

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A multi-source Laravel log file viewer for Livewire, built on top of livewire-datatable — real search/sort/pagination/export instead of hand-rolled pagination, per-entry detail in a Modal, redaction of sensitive data, and a purge action that's actually permission-gated and backed up.

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