Topic starter
02/08/2026 11:46 am
TL;DR: GenAI risk comes from how context, retrieval, and tool use interact rather than model quality alone, according to Akto. GenAI governance now has to constrain permissions, trace actions, and limit how far each workflow can go.
Editorial analysis by NHI Mgmt Group, based on content published by Akto: “GenAI Security Risks: Top Threats, Examples & Mitigation Strategies (2026 Guide)”.
Key questions
Q: Why do retrieval and context controls matter so much in GenAI security?
A: Because GenAI systems make decisions from whatever context they are given, and that context can include sensitive, manipulated, or overly broad material.
Q: What signals show that an AI agent is operating outside its intended purpose?
A: Look for mismatches across identity, data, model behaviour, posture, and environment.
Q: What breaks when GenAI systems are allowed to act without explicit authorization boundaries?
A: The system stops behaving like an assistant and starts behaving like an ungoverned operational actor.
Practitioner guidance
- Define explicit authority boundaries for AI systems Map each GenAI system to the exact data sources, tools, and workflows it may access, then remove any default broad permissions that let it operate across unrelated systems.
- Separate user content from system instruction paths Use retrieval and prompt handling designs that preserve instruction hierarchy, filter untrusted inputs, and prevent user-provided text from overriding operational guardrails.
- Scope agent permissions to task-specific actions Constrain autonomous or semi-autonomous AI actions so each workflow can only perform the minimum set of calls, updates, or queries required for that task.
Bottom line: GenAI risk comes from how context, retrieval, and tool use interact, not from model quality alone.
Glossary terms: GenAI-Driven Cybersecurity Risk, Prompt Injection (Agentic), Retrieval-augmented Generation, Agentic AI
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This topic was modified 2 days ago by NHI Mgmt Group
02/08/2026 12:56 pm
GenAI security is now an identity and authorization problem, not just a model risk problem. Once a system can read internal data, invoke tools, and trigger workflows, the control question becomes who or what is acting with authority. That intersects directly with IAM and PAM because the safe use of GenAI depends on scoped permissions, auditable action paths, and clear delegation boundaries. The practical conclusion is simple: treat GenAI as an operational actor, not a chat interface.
A few things that frame the scale:
- Half of companies using generative AI will deploy agentic AI by 2027, according to Deloitte's 2025 Technology Predictions.
A question worth separating out:
Q: How should organisations control sensitive data in GenAI tools?
A: Organisations should treat prompts, uploads, and model outputs as governed data flows, then apply classification, inspection, and logging at the point of use. The control objective is to stop sensitive information from entering AI workflows without visibility. That requires policy, access rules, and monitoring to work together, not as separate programmes.
👉 Read our full editorial: GenAI security risk is a governance failure, not a model flaw
This post was modified 2 days ago by NHI Mgmt Group