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Cisco will buy WideField Security and fold its identity and session telemetry tech into Splunk’s agentic AI SOC. The deal aims to track human and AI-agent actions in real time, tightening security around automated workflows. It follows Cisco’s recent picks of Astrix Security and Galileo Technologies.
- Cisco is acquiring WideField Security (fresh off an $11.3M Series A) to add real-time identity/session tracking for humans and AI agents into Splunk's agentic SOC.
- This is Cisco's third AI-security acquisition in recent months, following Astrix Security (agent protection) and Galileo Technologies (Splunk Observability).
- The tech aims to catch threats invisible to legacy tools, like authenticated AI processes going rogue, by feeding session-level signals into Cisco's Data Fabric.
- Customer demand for this isn't strong yet, but partners see deep identity tracking becoming essential as AI agents proliferate.
Zscaler unveiled a zero trust platform to secure autonomous AI agents’ data access, communications and device activity. It adds an AI Broker for agent-to-agent and data calls, endpoint AI threat detection, an AI Access Graph for mapping identities and data flows, and expanded AI Protect controls. This aims to give each AI agent its own identity, permissions and real-time monitoring.
- Zscaler launched a zero trust platform giving each AI agent its own identity, permissions, and real-time monitoring, built on four pieces: an AI Broker, Endpoint AI Security, an AI Access Graph, and expanded AI Protect controls.
- Dell'Oro Group forecasts the AI systems security market will grow from near zero to $8 billion by 2030, with nearly 60 vendors already competing.
- Analysts warn agents shouldn't inherit trust just because a user launched them—without unique identities and scoped permissions, compromised or misconfigured agents could move laterally and escalate privileges in seconds.
The article shows how real-world agentic AI deployments can blow through budgets because multi-step workflows use 5–30× more tokens per task than simple chatbots. It breaks down four hidden cost layers—LLM inference with re-sent context, context rot, tool orchestration, and infrastructure—and offers strategies to curb runaway spending before your production bill arrives.
- Agentic workflows use 5–30× more tokens per task than simple chatbot queries because each task triggers 10–20 model calls that reprocess the same context each time.
- Uber's Claude Code rollout to 5,000 engineers burned through the company's entire annual AI budget in one month, costing $500–$2,000 per engineer.
- Stanford research attributes 62% of agent inference costs to re-sending the same prompts and history on every call, and larger contexts also degrade model accuracy ("context rot"), triggering more retries.
- Routing simpler queries to cheaper, smaller models cut one team's monthly bill from $40,000 to $24,000, though falling per-token prices won't offset runaway consumption growth.