REAL-TIME RISK ASSESSMENT OF CODE CONTRIBUTIONS

    公开(公告)号:US20250045413A1

    公开(公告)日:2025-02-06

    申请号:US18230577

    申请日:2023-08-04

    Applicant: SAP SE

    Abstract: Contribution requests to a code repository are analyzed with a machine learning model before publishing. The machine learning model can be trained with past metadata of the contributor. Metadata can be extracted from the requests to determine whether the request is atypical for the contributor via a risk score. Requests determined to be atypical can be flagged for action by a security manager. Realtime assessment of code contributions can increase overall software security in a software development context.

    AUTOMATED REVOCATION SYSTEM FOR LEAKED ACCESS CREDENTIALS

    公开(公告)号:US20240143797A1

    公开(公告)日:2024-05-02

    申请号:US17975290

    申请日:2022-10-27

    Applicant: SAP SE

    CPC classification number: G06F21/604

    Abstract: Techniques for automatically revoking leaked access credentials are disclosed. In some embodiments, a computer system may receive an indication that a credential for accessing a resource has been leaked, where the credential has been leaked by being included in content that has been published on an online service or has been stored in a shared folder of the online service. The computer system may then determine that the credential is effective in accessing the resource, and, in response to the determining that the credential is effective, trigger a revocation of the credential, the revocation of the credential causing the credential to no longer be effective in accessing the resource.

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