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02/08/2026 12:28 pm
TL;DR: AI application security must now cover prompt injection, data poisoning, model theft, and other AI-specific threats that conventional AppSec often misses, according to Akto. The governance challenge is no longer just securing code and APIs, but controlling model behavior, data flows, and agent interactions across the full lifecycle.
NHIMG editorial — based on content published by Akto: AI Application Security: A Modern Approach to Safer AI Systems
Questions worth separating out
Q: How should security teams implement AI evaluation in production workflows?
A: Security teams should treat AI evaluation as a continuous control, not a pre-launch checklist.
Q: Why do AI systems create identity and access risk beyond traditional AppSec?
A: Because AI systems often act through delegated access.
Q: What breaks when AI outputs are validated without runtime monitoring?
A: Offline validation can confirm expected behaviour in test conditions, but it cannot detect live abuse after deployment.
Practitioner guidance
- Bound AI tool permissions to task scope Define the minimum set of tools, APIs, and datasets each AI workflow can touch, then revoke anything that is not required for the task.
- Add prompt and output controls to the SDLC Treat prompt injection, unsafe output handling, and retrieval poisoning as design-time risks.
- Instrument runtime monitoring for AI-specific abuse Log model calls, tool invocations, and anomalous response patterns into SIEM workflows so suspicious behaviour can be triaged quickly.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- How Akto positions AI firewalls, runtime monitoring, and SIEM integration inside the AI application security lifecycle
- The article's side-by-side comparison of traditional AppSec and AI AppSec across attack surface, tools, and assessment methods
- The practical implementation framing for AI security in DevSecOps pipelines, including model validation and continuous monitoring
- Akto's own view of how its agentic AI suite fits into API security and AI application protection
👉 Read Akto's analysis of AI application security for safer AI systems →
AI application security and agent governance: are your controls keeping up?
Explore further
02/08/2026 1:26 pm
AI application security is becoming an identity governance problem, not just an AppSec problem. Once a model can call tools, query data, or trigger workflows, it inherits a form of runtime authority that must be governed like any other privileged system. The practical issue is not only whether the model is secure, but whether its access is bounded, reviewable, and revocable in the same way as sensitive service access.
A question worth separating out:
Q: When should organisations treat an AI agent as a privileged system?
A: Organisations should treat an AI agent as privileged whenever it can reach production data, administrative tools, or sensitive workflows without direct human approval for each step. At that point, the agent is no longer a passive automation helper. It becomes a governed identity whose permissions, logs, and exceptions need the same scrutiny as other high-risk access.
👉 Read our full editorial: AI application security is shifting toward agent and model governance
