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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.
NHIMG editorial — based on content published by Akto: GenAI Security: Risks, Frameworks and Best Practices for 2026
By the numbers:
- As adoption increases, about 61% of organizations are encountering new threats associated with large language models and generative tools.
- 80% of organisations report their AI agents have already performed actions beyond their intended scope, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
- 96% of technology professionals identify AI agents as a growing security threat, and 66% believe this risk is immediate.
Questions worth separating out
Q: How should security teams govern AI agents that can access enterprise systems?
A: Security teams should govern AI agents as non-human identities with explicit ownership, scoped privileges, and continuous monitoring.
Q: Why do AI models create more security risk than traditional applications?
A: AI models create more risk because they can be manipulated through prompts, poisoned data, and connected APIs, not just through code defects.
Q: What do teams get wrong about prompt injection in AI assistants?
A: They treat it as a content safety issue instead of an access issue.
Practitioner guidance
- Map every AI system and connector Build an inventory of deployed GenAI tools, the data sources they query, and the external or internal systems they can call.
- Constrain tool access to task scope Assign only the minimum tool permissions each agent needs for a specific workflow, then separate prompt ingestion from execution paths.
- Require output validation before reuse Treat generated text, code, and summaries as untrusted until they are checked by review, policy filters, or automated scanning.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- Step-by-step guidance on mapping GenAI systems, connectors, and workflows across the enterprise
- Examples of prompt injection, data leakage, and unsafe tool-use scenarios in production AI
- Breakdowns of runtime monitoring, logging, and agent security controls for live environments
- Implementation-oriented discussion of AI security tools and workflow-level protection patterns
👉 Read Akto's full analysis of GenAI security risks, frameworks, and best practices →
GenAI security in 2026: are your agent controls keeping up?
Explore further
02/08/2026 1:19 pm
GenAI security is now an identity governance problem, not just an application security problem. Once an AI system can retrieve data or invoke tools, it behaves like an access-bearing entity that must be scoped, monitored, and reviewed. That makes entitlement design, credential handling, and auditability central to AI security. Teams that keep treating agents as simple software objects will miss the control plane entirely.
A question worth separating out:
Q: How can organisations tell whether AI governance is actually working?
A: Organisations can tell AI governance is working when they can inventory every agent, explain its purpose, show who owns it, and prove that permissions are tightly scoped. If those four things are missing, the programme has policy language but not operational control. Auditors will notice the gap quickly.
👉 Read our full editorial: GenAI security in 2026: why AI agent governance is now central
