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15/08/2026 6:14 pm
TL;DR: Modern AI systems create a broad threat surface spanning bias, privacy leakage, misinformation, and runtime abuse, with special emphasis on LLM failure modes such as prompt injection, jailbreaking, and training data poisoning, according to Lakera. The identity angle is clear: once AI systems can access data, tools, or credentials, governance has to extend beyond model safety into access control, secrets handling, and runtime authorization.
NHIMG editorial — based on content published by Lakera: AI risks and the critical challenges of artificial intelligence
Questions worth separating out
Q: How should security teams govern AI models that can call tools and access data?
A: Security teams should govern AI models as non-human identities with named owners, limited scope, short-lived credentials, and continuous authorization.
Q: Why do conversational AI systems create new identity and access risks?
A: Because they can combine data retrieval, decision-making, and execution in a single interaction.
Q: What breaks when prompt injection reaches a tool-using AI agent?
A: What breaks is the assumption that the model's output is low impact.
Practitioner guidance
- Separate context from execution Route prompts, retrieval results, and tool permissions through different control layers so that untrusted text cannot directly trigger privileged actions.
- Issue workload identities to AI systems Assign each AI service its own workload identity, then scope tokens and API keys to a single application, environment, and purpose.
- Approve data sources before training or retrieval Create an intake process for training sets, embeddings, and retrieval corpora that validates provenance, ownership, and integrity before the data reaches the model pipeline.
What's in the full article
Lakera's full article covers the operational detail this post intentionally leaves for the source:
- Specific examples of prompt injection, jailbreaking, and in-context learning failures in live GenAI workflows
- A deeper breakdown of training data poisoning, model misuse, and content moderation failure modes
- Practical guidance on monitoring LLM behaviour when systems begin to act on retrieved content or tool output
- Implementation-oriented context on how AI security controls differ from traditional application security
👉 Read Lakera's analysis of AI risk categories and LLM failure modes →
AI risks and identity controls: are your governance models keeping up?
Explore further
15/08/2026 6:20 pm
AI governance debt is now an identity problem. Organisations often frame GenAI risk as a model safety issue, but the stronger control failure is that AI systems are being granted access without lifecycle governance. Once an AI application can query data, trigger actions, or call services, the programme needs the same discipline used for privileged accounts and non-human identities. The practical conclusion is that AI risk registers must include identity, privilege, and credential ownership.
A question worth separating out:
Q: How do teams know whether AI governance is actually working?
A: Look for evidence that every AI interaction can be traced end to end, from identity and intent to output and enforcement. If auditors can ask for a transaction and receive a complete record in hours, not weeks, the programme is producing usable control evidence rather than just documentation.
👉 Read our full editorial: AI risks are becoming identity risks in generative systems
