Topic starter
25/10/2026 11:06 am
TL;DR: AI risk now centers on data access, retrieval paths and delegated actions, Protecto says, with prompt injection, RAG leakage, model poisoning and agentic abuse all in play. Security teams need to govern what these systems can reach, then constrain what they can do and disclose.
Editorial analysis by NHI Mgmt Group, based on content published by Protecto: “Top AI Security Vulnerabilities to Watch Out For in 2026”.
By the numbers:
- An AI agent identified 77% of vulnerabilities in real software, placing it in the top 5% of human cybersecurity competitors.
- 73% of security professionals say AI-powered cyber threats are already significantly impacting their organizations.
Key questions
Q: Why do AI agents increase operational risk compared with chatbots?
A: AI agents can take actions, not just generate text, so a bad decision can become an unauthorised workflow, a data exposure, or a transactional error.
Q: How should teams reduce data leakage in RAG outputs?
A: They should control leakage before the model responds by redacting or tokenising sensitive values, enforcing response authorisation, and scanning both prompts and outputs for unsafe disclosures.
Q: What breaks when AI systems can reach too many data sources?
A: The main failure is not that the model becomes inaccurate, but that authorised access turns into unintended disclosure.
Practitioner guidance
- Tokenize sensitive data before AI ingestion Mask or tokenize PII, PHI and other regulated data before it enters prompts, embeddings or training datasets so the model never has raw material to leak.
- Enforce retrieval authorisation for RAG Apply row-level security, namespace isolation and per-request retrieval checks so similarity search cannot return content the requester is not entitled to see.
- Constrain agent authority to task scope Grant each AI agent only the data sources, tools and actions needed for the current task, and revoke that access when the task ends.
Bottom line: The article shows that AI risk is now defined by data access, retrieval paths and delegated actions, not just by malicious prompts.
Glossary terms: Prompt Injection (Agentic), Retrieval-augmented Generation, Context-based access control, Shadow AI
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This topic was modified 4 hours ago by NHI Mgmt Group
27/10/2026 7:15 pm
Prompt filtering is the wrong primary control when data access remains broad. The article’s central point is that prompt injection becomes damaging only when the model can reach sensitive records, documents or tool outputs in readable form. That shifts the governance question from “can the prompt be malicious?” to “what data is the model allowed to see at all?” Practitioners should treat data reachability as the first-order control boundary.
A few things that frame the scale:
- Breaches involving third parties rose to 48% of all breaches, a 60% increase on the previous year, according to Verizon's 2026 Data Breach Investigations Report.
A question worth separating out:
Q: What should organisations do when AI agents are being built outside IT oversight?
A: Organisations should create a controlled path for discovery, approval, and ongoing monitoring of AI agents before they are used for business tasks. That means bringing security, IT, and business owners into the review process, validating data access and permissions, and checking for unauthorized data access or policy violations. The goal is to keep innovation visible and governed.
👉 Read our full editorial: AI security vulnerabilities in 2026 shift toward data access and supply chains
This post was modified 4 hours ago by NHI Mgmt Group