Globally, teams in risk, procurement, and compliance are under pressure to manage geopolitical risks and business risks. Third-party risks are impacted by the complexity of domestic and international businesses, as well as complex and diverse regulations. It is crucial that companies proactively manage third-party relationships. This cutting-edge platform, powered by D&B Data Cloud's 520M+ Global Business Records with 2B+ annual updates for third-party risks, is an AI-powered solution that mitigates and monitors counterparty risk on a continual basis. D&B Risk Analytics uses best-in class risk data, including alerts for high-risk purchases and match points of more than a billion. This helps to drive informed decisions. Intelligent workflows allow for quick and thorough screening. Receive alerts on key business indicators.
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Compliance work eats engineering time. Hyperproof exists to give that time back by automating the parts of GRC that don't need a human: pulling evidence out of GitHub, Jira, ServiceNow, Snyk, and cloud storage on a schedule, running recurring tests against high-frequency controls, and kicking off a task automatically the moment something fails instead of waiting for the next audit cycle to find out.
Under the hood, Hyperproof maps one control to 160+ frameworks (SOC 2, ISO 27001, HIPAA, NIST, and others), so a control tested once can satisfy several standards instead of forcing teams to rebuild the same work per framework. AI agents handle the first pass on evidence review and gap-flagging, leaving humans to make the actual judgment calls rather than hunting down documentation.
Teams using it report cutting audit prep by roughly 350 hours a year, a 66% drop in duplicate controls, and about $150K saved annually on control orchestration. It also scales to messier org charts, with the ability to scope controls by business unit or entity instead of flattening everything into one program.
Built in 2018 out of the Seattle area, Hyperproof is used by engineering and security-heavy orgs like Reddit, Fortinet, Appian, and Outreach that are tired of treating compliance as a manual, spreadsheet-and-email process and want it to run more like the rest of their infrastructure: automated, monitored, and auditable.
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Analytica
Beautiful dashboards and reports are available in BI tools that allow users to examine patterns in historical data. Past data can provide insights. It cannot be prescriptive. Model-Driven decision making is the only way to get a better understanding of what could happen in unusual situations and how to make it happen. Analytica is an innovative visual software environment that allows you to build, explore, and share quantitative decision models that produce prescriptive results. Transcend cumbersome spreadsheets. Analytica's flexibility, power, flexibility, and clarity are a revelation. Analytica makes it easy to create transparent models in fractions of the time required for procedural languages such as R or Python. Analytica provides insights, not just numbers. Agile modeling can be used to create models that support business decision-making. Probabilistic simulations are efficient and accurate in estimating risk and uncertainty. Smart sensitivity analysis reveals what is important and why.
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MADe
Reduce technical engineering-related risks that can affect performance, operational reliability, and maintenance costs through the use of modeling, analysis, and decision support processes. The MADe system facilitates improved decision-making regarding the design and maintenance of safety and mission-critical equipment throughout the entire product lifecycle. By employing interdependent analysis capabilities, it addresses the technical, operational, and economic needs of the system's operators and maintainers, thereby mitigating risks effectively. Furthermore, MADe serves as a comprehensive analysis tool that produces the necessary documentation for Airworthiness certification. Conducting analysis concurrently with the design phase enhances the efficiency of the certification process significantly. Additionally, MADe meticulously tracks the origins of all parameters utilized in its analyses, ensuring a robust mechanism for evaluating the quality of data that informs engineering decisions and assessments. This attention to detail ultimately strengthens the reliability and effectiveness of the engineering processes involved.
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