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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.
NHIMG editorial — based on content published by Akto: AI Security Risks: Top Threats, Examples and Mitigation Strategies
By the numbers:
- 78% of CISOs saying AI-driven threats are already having major impact on their organizations.
- AI agents have already performed actions beyond their intended scope in 80% of organisations, including accessing unauthorised systems (39%), inappropriately sharing sensitive data (31%), and revealing access credentials (23%).
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 agents create more risk than traditional automation?
A: AI agents create more risk because they can interpret context, choose actions, and invoke tools autonomously.
Q: What breaks when organisations ban shadow AI instead of governing it?
A: Bans often push AI use into personal accounts, unmanaged devices, and hidden workflows, which removes visibility from security and makes data exposure harder to detect.
Practitioner guidance
- Discover all AI systems and connected identities Build a live inventory of AI models, agents, plugins, MCP connections, service accounts and tokens.
- Scope every AI agent to task-bound access Apply least privilege to agents, copilots and automated workflows, with separate entitlements for retrieval, write actions and tool execution.
- Enforce runtime guardrails on prompts and tools Filter untrusted content, constrain tool invocation and block unsafe actions at runtime rather than relying only on pre-deployment testing.
What's in the full article
Akto's full blog covers the operational detail this post intentionally leaves for the source:
- Step-by-step mitigation guidance for prompt injection, shadow AI and data poisoning across live AI environments
- Practical runtime protection examples for filtering prompts, constraining tool use and detecting unsafe agent behaviour
- A fuller breakdown of AI security posture management for discovery, monitoring and policy enforcement
- Source examples and mitigation summaries for AI systems connected to MCP, plugins and external tools
👉 Read Akto's analysis of the top AI security risks and mitigation strategies →
AI security risks in 2026: are your controls keeping up?
Explore further
02/08/2026 1:18 pm
AI security has become an access governance problem, not just a model safety problem. Once AI systems connect to databases, cloud services and plugins, the decisive question is what they can reach and under what conditions. That makes IAM, PAM and NHI controls central to AI risk management, especially where agents can act autonomously or on behalf of a user. Practitioners should treat AI permissions as live entitlements, not static configuration.
A question worth separating out:
Q: How should security teams reduce prompt injection risk in AI agents?
A: Security teams should reduce prompt injection risk by constraining what enters the context window, limiting tool permissions, and separating untrusted retrieval content from privileged instructions. The practical goal is not perfect detection. It is to ensure that a successful injection cannot trigger wide data access, uncontrolled writes, or irreversible actions through a delegated identity.
👉 Read our full editorial: AI security risks in 2026 expose the limits of static controls
