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06/06/2026 11:31 am
TL;DR: Generative AI is reactive and content-focused, while agentic AI can take autonomous actions through tools, memory, and MCP connections, creating a wider attack surface and IAM attribution gaps according to WitnessAI. The security model changes from output control to runtime governance because access, intent, and delegated action can diverge within the same session.
NHIMG editorial — based on content published by WitnessAI: Agentic AI vs. Generative AI security analysis
By the numbers:
- By 2028, 33% of enterprise software applications will include agentic AI.
- WitnessAI says its platform can detect more than 4,000 AI applications across the enterprise.
Questions worth separating out
Q: How should security teams govern agentic AI differently from generative AI?
A: Security teams should govern generative AI as a content and data-handling problem, but agentic AI as an identity and runtime-authority problem.
Q: Why do agentic AI systems create more IAM risk than chatbots?
A: Agentic systems can call tools, retain memory, and continue acting without a new human decision for every step.
Q: What breaks when AI agents share memory and tool access across sessions?
A: Shared memory and tool access can preserve bad instructions, permissions, or context long after the original interaction.
Practitioner guidance
- Separate content risk from action risk Create distinct control paths for generative outputs and agentic execution.
- Inventory every AI identity and connector Build a current register of sanctioned and shadow AI systems, including agents, MCP servers, APIs, and data sources.
- Trace delegation across the AI workforce Log the full chain of action from human initiator to agent and sub-agent, including tool calls and handoffs.
What's in the full article
WitnessAI's full analysis covers the operational detail this post intentionally leaves for the source:
- Network-level discovery methods for AI activity across employees, agents, and embedded copilots
- The bidirectional runtime inspection model for prompts, responses, and tool calls before execution
- Implementation detail on intent-based policy enforcement, including allow, warn, block, and tokenize decisions
- Architecture notes on single-tenant deployment, BYOK, and SOC 2 Type II controls for AI traffic
👉 Read WitnessAI's analysis of agentic AI and generative AI security differences →
Agentic AI vs. generative AI: what IAM teams are missing?
Explore further
06/06/2026 12:21 pm
Agentic AI turns identity from a static access problem into a runtime authority problem. Generative AI can be governed as a content system because the human remains the final decision point. Agentic AI changes that because the system can choose actions, tools, and sequencing inside the workflow. IAM and security teams therefore need to stop thinking only in terms of prompt safety and start thinking about who is authorised to initiate action at machine speed.
A few things that frame the scale:
- 98% of companies plan to deploy even more AI agents within the next 12 months, despite documented rogue behaviour in 80% of current deployments, according to AI Agents: The New Attack Surface report.
- Only 52% of companies can track and audit the data their AI agents access, leaving 48% with a complete blind spot for compliance and breach investigation.
A question worth separating out:
Q: What should organisations do when an AI agent delegates work to other agents?
A: Organisations should require a traceable delegation chain that records the initiating identity, each handoff, each tool call, and the business purpose of downstream access. Without that chain, teams cannot determine whether the final action still matched the original authorization or whether privilege expanded silently as the workflow progressed.
👉 Read our full editorial: Agentic AI and generative AI need different identity controls
