Tag: AI Workloads

Aembit’s new Credential Provider automates Claude API Workload Identity Federation, retiring static keys for short-lived tokens.
The MCP authorization spec sets a new standard for securing non-human AI agents – with lessons for anyone building autonomous, scalable systems.
AI agents are workloads, but traditional workload identity alone can miss the user, task, and runtime context needed to govern dynamic agent access.
A down-to-earth primer to help engineers make sense of agentic AI architecture and where things stand today.
As AI moves from chat windows to enterprise systems, data leakage becomes an identity and access problem.
Eliminating static API keys is real progress – but securing one credential surface is not the same as governing workload access at scale.
An early IETF draft hints at how identity infrastructure may evolve once autonomous software starts acting inside enterprise environments.
The global research and advisory firm is pushing the industry toward a more practical model for securing AI agents and non-human access.
AI agents exchange sensitive contexts across MCP servers in seconds. Without context-aware auditing, you can’t trace who accessed what.
Static access rules fail in dynamic MCP environments. Context-based access control evaluates identity, context and resources in real time.