Cybersecurity threats, protection strategies, and best practices
Found 104 articles
Implement automatic reviews to ensure your AI agent skills are secure before deployment, mitigating the risks of malicious code and security breaches.
As AI agents gain new operational capabilities, the need for kernel-level monitoring becomes essential to mitigate emerging security risks.
Structured logging is essential for effective observability in distributed systems, yet many teams struggle to implement it efficiently.
Efficiently identifying sensitive data before masking is essential in Test Data Management to prevent privacy breaches during application development.
Organizations using Microsoft Power Platform face new security risks from citizen development, highlighting the need for effective governance and control measures.
Adopting zero-trust principles during cloud migrations can prevent costly security rework and streamline the process for enterprises.
Google's GKE Agent Sandbox offers a secure environment for executing untrusted code, revolutionizing how we manage application security in AI platforms.
Branch networks demand new security strategies, moving beyond traditional models to embrace policy-driven approaches for enhanced protection.
API security must adapt as AI agents operate autonomously, raising new vulnerabilities with their ability to chain calls unpredictably.
Traditional JMeter tests often miss critical factors, compromising their ability to accurately reflect production loads and user behavior.
Improve Spark performance on Databricks by focusing on shuffle tuning, skew mitigation, and Z-Ordering for efficient data management.
A critical look at how autonomous systems can be exploited through misinterpreted instructions, emphasizing the need for tighter security measures.
The New York Mets and Novig have entered a historic partnership to fuse baseball with prediction markets, enriching fan interaction and experience.
Organizations face a trust accounting crisis, as the traditional focus on identity verification often neglects ongoing scrutiny in security.
Traditional accuracy metrics fall short for agentic AI; new frameworks are needed to assess their complex decision-making processes.
Transforming RAG workflows is now achievable in just hours with Azure AI Foundry, significantly reducing the setup complexity of LLM grounding.
Agentic test creation redefines quality assurance, distinguishing itself from standard AI test generation by enhancing test relevance and effectiveness.
A significant percentage of organizations are unaware of unauthorized AI agents in their systems, raising pressing security concerns.
Exploring advanced strategies for AI security, this piece critiques traditional defenses against prompt injection attacks and suggests proactive measures.
Kubernetes teams must shift focus to container runtime security, addressing critical vulnerabilities that arise once workloads are in production.