Security | Threat Detection | Cyberattacks | DevSecOps | Compliance

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AI agent authentication is the process of verifying that an autonomous agent is the identity it claims to be before it interacts with infrastructure, applications, APIs, or data. Because agents often act on behalf of users, services, or workflows, authentication must be paired with delegated context and downstream authorization controls that determine what the agent is allowed to do, which resource it can access, and how long that access should last.

A developer on your team just told Claude Code to connect to a new MCP server, the protocol coding agents use to reach organizational tools and data. Nobody in security reviewed it. Nobody in security even knows it happened. For two-thirds of enterprises, the primary obstacle to scaling agentic development isn’t budget or headcount — it’s security risk.

Identities in the modern enterprise are increasingly less human and more autonomous, powered by the rise in AI agents, APIs and other non-human identities (NHIs). The result is often an entitlement sprawl, where operations take place without human oversight and with privileged access. This non-linear evolution has meant many businesses have had to respond using bolted-on tools, rather than one unified, enterprise-grade platform.

DORA compliance for mobile applications is the process of identifying, testing, and documenting mobile ICT risks in line with Regulation (EU) 2022/2554, covering Articles 8, 9, 10, 24, and 25, through vulnerability assessments, security testing, and audit-ready evidence generation that financial institutions can present to regulators, auditors, and internal governance bodies.

Hugging Face was breached by a rogue OpenAI agent last week, and the intrusion continues to deliver insights and understanding. The Hugging Face team published a detailed timeline along with a 17,600-event trace streaming replay visualizing what happened, and it’s marvelously and intoxicatingly detailed. I recommend you read it if you have the time.

Every company you depend on is standing up AI right now. Chatbots, copilots, RAG pipelines, agent frameworks, model gateways. The pressure to ship something with "AI" attached to it is enormous, and it is pushing infrastructure into production faster than security teams can review it.

OpenAI's models escaped a benchmark sandbox and ended up inside Hugging Face's production systems. The attack made history; the openings it used were reusable credentials and flat internal access, and those are fixable now.

Generative AI has quickly moved from experimentation to everyday business use. Employees use AI tools to create content, write code, analyze data, automate repetitive tasks, and interact with business applications in entirely new ways. At the same time, software vendors are embedding AI capabilities into the products organizations already use every day, often without requiring users to adopt separate tools.

Prompt injection in a standard LLM interaction produces bad output. The model says something it shouldn’t. The damage stays contained to text. Prompt injection in an MCP environment is a different problem. Agents built on the Model Context Protocol don’t just generate responses. They call tools. They write files, query databases, send emails, execute code, invoke APIs.