Topic starter
02/08/2026 12:17 pm
TL;DR: GenAI security now spans prompt injection, data leakage, over-permissioned tool access, and shadow AI, because enterprise AI systems increasingly interact with sensitive data and internal workflows, according to Akto. The security problem is no longer model output alone but the access, context, and action paths that AI agents can traverse without tight governance.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “GenAI Security: Risks, Frameworks and Best Practices for 2026”.
By the numbers:
- 61% of organisations are encountering new threats associated with large language models and generative tools.
Key questions
Q: How should security teams govern AI agents that call internal tools from Kubernetes?
A: They should separate runtime containment from identity governance.
Q: Why do AI agents increase the risk of overpermissioning?
A: AI agents increase that risk because teams often expand scopes to unblock early use cases, then keep those permissions because the original need is hard to prove or remove.
Q: What are the signs that shadow AI is creating governance and compliance gaps?
A: Common signs include limited visibility into who is using AI tools, no reliable audit trail for prompts and outputs, unclear data residency, and inconsistent handling of sensitive information.
Practitioner guidance
- Inventory all AI agents and connectors Map every deployed GenAI system, the tools it can call, the data sources it can reach, and the workflows it influences.
- Scope agent permissions to task boundaries Remove broad connector access and force sensitive actions through explicit approval, policy checks, or separate execution paths.
- Separate user input from execution logic Apply instruction hierarchy, retrieval filtering, and output inspection so untrusted content cannot directly shape tool calls or backend actions.
Bottom line: GenAI security now fails most often at the boundary between model output and downstream action, where over-permissioned tools and weak workflow controls turn a prompt into an enterprise event.
Explore further
View Full Forum → | NHI Foundation Course → | Our Services → | Read the full analysis →
02/08/2026 1:19 pm
Agent governance is now an identity problem, not just an AI safety problem. Once AI systems can call tools, query data, and trigger workflows, they need lifecycle, privilege, and audit control comparable to other non-human identities. The article captures the market's real shift: risk is moving from model output to delegated action. Practitioners should treat AI agents as governed actors, not feature enhancements.
A few things that frame the scale:
- 69% of security leaders agree identity management must fundamentally shift to address agentic AI systems, according to the 2026 Infrastructure Identity Survey.
A question worth separating out:
Q: What should organisations review first before allowing GenAI into production?
A: Start with inventory and privilege. Identify every model, agent, connector, and data source, then decide which actions truly need to exist and which should require approval or be removed. Without that baseline, policy, monitoring, and compliance controls are all built on incomplete assumptions.
👉 Read our full editorial: GenAI security in 2026: why AI agent governance is now central