Topic starter
02/08/2026 12:16 pm
TL;DR: AI systems are now connected to MCP, plugins, cloud platforms and multi-agent ecosystems, which expands the attack surface beyond conventional software and makes prompt injection, shadow AI, data poisoning and supply chain abuse harder to contain, according to Akto. Static security models are no longer enough when tools can act, retrieve and expose data at runtime.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “AI Security Risks: Top Threats, Examples and Mitigation Strategies”.
By the numbers:
- 78% of CISOs say AI-driven threats are already having a major impact on their organisations.
Key questions
Q: What breaks when AI agents rely on static data residency controls?
A: Static controls break because they assume data follows a declared path, while AI agents choose tools, retrieve content, and delegate work at runtime.
Q: Why do mobility AI systems increase identity and access risk?
A: Mobility AI systems increase identity and access risk because they connect cloud services, edge devices, vendor support paths, and machine-to-machine control loops.
Q: What do security teams get wrong about Shadow AI?
A: They often treat Shadow AI as an approval problem for software, when it is usually also an identity problem.
Practitioner guidance
- Implement runtime access controls for AI agents Bind each agent to task-scoped permissions, separate instruction inputs from untrusted content, and require policy checks before tool execution.
- Inventory shadow AI and connected tools Discover all AI apps, assistants, plugins and agent workflows that can reach enterprise data, then assign owners and approval paths.
- Constrain third-party AI integrations Review every external model, API, plugin and dataset connection for data exposure, privilege inheritance and hidden execution paths.
Bottom line: AI security risk in 2026 is being driven by connected systems that act at runtime, not by isolated models that can be checked once and forgotten.
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02/08/2026 1:18 pm
Static control models are now structurally misaligned with agentic AI. The problem is not that traditional controls are weak in general, but that they assume stable applications and predictable decision paths. Agentic systems introduce runtime choice, external tools and mutable context, which means control has to move closer to execution time. For identity teams, that makes authorisation, session scope and delegated trust the decisive issues, not periodic review alone.
A few things that frame the scale:
- 67% of organisations still rely heavily on static credentials despite the risks they pose to agentic AI deployments, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What is the difference between prompt injection and normal user input?
A: Normal user input is meant to request a service, while prompt injection is designed to override instructions, steer behaviour or expose hidden context. In AI systems with tool access, that difference matters because the payload can change what the agent does, not just what it says. That makes context isolation and instruction hierarchy essential controls.
👉 Read our full editorial: AI security risks in 2026 expose the limits of static controls