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Privacy spotlight: Retain data in Grail with 1-day precision, for up to 10 years

Dynatrace

Streamline privacy requirements with flexible retention periods Data retention is a critical aspect of data handling, and it’s not just about privacy compliance—it’s about having the flexibility to optimize data storage times in Grail for your Dynatrace use cases.

Storage 161
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What the SEC cybersecurity disclosure mandate means for application security

Dynatrace

This blog provides explains the SEC disclosure and what it means for application security, best practices, and how your organization can prepare for the new requirements. For instance, the FTC has been increasingly active in pursuing penalties for data security and privacy practices. Regulatory risks are also increasing.

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Enterprise Cloud Security Strategy For 2024

Scalegrid

To ensure the protection of their sensitive data across public, private, and distributed clouds, large organizations implement various technologies, policies, procedures, and controls as part of their comprehensive approach to enterprise cloud security.

Strategy 130
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Building a responsible data capture policy

Dynatrace

Here’s just a few of his “guard rails” that I took away: Legality isn’t a high enough bar – Compliance with relevant laws, standards, and company policies (e.g. Now that we have some best practice guard rails for data capture generally, let’s apply each to data capture in Dynatrace…. Guard rails in action.

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Pre-Deployment Policy Compliance

Abhishek Tiwari

However, amidst the drive for speed, ensuring policy compliance is often overlooked, leading to potential security vulnerabilities and compliance risks. Pre-deployment policy compliance, supported by policy as code frameworks such as Sentinel, Open Policy Agent (OPA), Conftest, etc.

AWS 52
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How Data Inspires Building a Scalable, Resilient and Secure Cloud Infrastructure At Netflix

The Netflix TechBlog

This operational component places some cognitive load on our engineers, requiring them to develop deep understanding of telemetry and alerting systems, capacity provisioning process, security and reliability best practices, and a vast amount of informal knowledge about the cloud infrastructure.

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Cloudy with a high chance of DBMS: a 10-year prediction for enterprise-grade ML

The Morning Paper

When it comes to leveraging ML in enterprise applications, especially in regulated environments, the level of scrutiny for data handling, model fairness, user privacy, and debuggability will be substantially higher than in the first wave of ML applications. Flock treats ML models as software artefacts derived from data.