managed across the AI lifecycle<\/strong>, not treated as a one-time pre-launch checklist.<\/p>\nThis second blog closes the loop by adding what most organizations actually need to succeed: a repeatable operating model for governing and measuring risk over time, and a realistic 30\/60\/90 rollout plan<\/strong> to implement controls without slowing innovation. Read together, Part 1 and Part 2 deliver the complete, end-to-end picture\u2014what breaks, what prevents it, and how to keep it secure as adoption scales<\/strong>.<\/p>\nUnderstanding The 10 Failure Modes (LLM 06 \u2192 LLM 10)<\/h2>\n
The OWASP LLM Top 10 represents the most critical security risks facing applications that leverage large language models. Unlike traditional application security concerns, these vulnerabilities arise from the unique characteristics of LLMs: their training on vast datasets, their ability to generate content, and their integration into complex enterprise workflows.<\/p>\n
![\"OWASP](\"https:\/\/www.solix.com\/blog\/wp-content\/uploads\/2026\/02\/OWASP-GenAI-Risk-Map-1024x576.png\" "\\"\\"")
<\/p>\n
LLM06: Overpowered agents (Excessive Agency)<\/h3>\n
LLMs granted too much autonomy or access to sensitive functions can take unintended actions, escalate privileges, or cause significant business impact through autonomous decision-making. OWASP defines excessive agency as enabling damaging actions due to unexpected\/ambiguous\/manipulated outputs, and points to \u201cexcessive functionality, permissions, autonomy\u201d as common root causes.<\/p>\n
![\"quote](\"https:\/\/www.solix.com\/blog\/wp-content\/uploads\/2026\/02\/quote-6-1024x187.png\" "\\"\\"")
<\/p>\n
Enterprise Security Controls<\/h3>\nPrevent<\/h4>\n\n- Give the agent only the minimum access it needs (prefer read-only; limit data and actions).<\/li>\n
- Require extra verification and set strict limits for risky actions (approvals, amount caps, confirmations).<\/li>\n
- Provide emergency stop access and a \u201ctest mode\u201d that simulates actions without applying changes.<\/li>\n<\/ul>\n
Detect<\/h4>\n\n- Learn normal agent activity and flag unusual patterns (such as excessive actions, unusual hours, or high-impact changes).<\/li>\n
- Regularly review and audit LLM permissions, removing unused access.<\/li>\n<\/ul>\n
Respond<\/h4>\n\n- Immediately disable tool access, revoke its credentials, and tighten permissions\/rules after review.<\/li>\n
- Define explicit allowlists of permitted actions for each LLM application.<\/li>\n<\/ul>\n
LLM07: Internal logic exposed (System Prompt Leakage)<\/h3>\n
System prompts contain critical instructions, business logic, and security controls. System prompt leakage is when internal instructions, routing logic, or hidden guardrails are exposed in responses. When leaked, attackers can reverse-engineer defenses, identify bypasses, or gain insights into proprietary processes.<\/p>\n
Note: System prompt leakage is often triggered by prompt injection (LLM01) and can result in sensitive information disclosure (LLM02). We\u2019re treating it as a separate failure mode here because the primary mitigations\u2014prompt compartmentalization and keeping secrets out of prompts\u2014are distinct and worth calling out explicitly.<\/p>\n
![\"quote](\"https:\/\/www.solix.com\/blog\/wp-content\/uploads\/2026\/02\/quote-7-1024x155.png\" "\\"\\"")
<\/p>\n
Enterprise Security Controls<\/h3>\nPrevent<\/h4>\n\n- Don\u2019t put passwords or keys in system instructions; keep them in secure storage with limited access.<\/li>\n
- Split instructions into separate parts (rules, business steps, tool steps) instead of one big prompt.<\/li>\n
- Enforce rules with controls in the app (access checks, filters), not just with \u201cthe model should obey.\u201d<\/li>\n<\/ul>\n
Detect<\/h4>\n\n- Run regular automated tests that try to extract hidden instructions.<\/li>\n
- Flag repeated attempts to get the bot to reveal its hidden rules.<\/li>\n<\/ul>\n
Respond<\/h4>\n\n- Replace the system instructions and change any exposed passwords\/keys.<\/li>\n
- Review what was revealed, then tighten separation and access controls to prevent a repeat.<\/li>\n<\/ul>\n
LLM08: RAG retrieval as a backdoor (Vector & Embedding Weaknesses)<\/h3>\n
Retrieval-Augmented Generation (RAG) systems rely on vector databases and embeddings. Vulnerabilities in these components can lead to unauthorized data access, poisoning attacks, or inference of sensitive information from embeddings.<\/p>\n
![\"quote](\"https:\/\/www.solix.com\/blog\/wp-content\/uploads\/2026\/02\/quote-8-1024x244.png\" "\\"\\"")
<\/p>\n
Enterprise Security Controls<\/h3>\nPrevent<\/h4>\n\n- Check access rules every time a document is fetched.<\/li>\n
- Tenant isolation where required (separate indexes or strict partitioning).<\/li>\n
- Remove or hide sensitive details before turning documents into embeddings.<\/li>\n
- Enforce access control at retrieval time: store each chunk with metadata (e.g., tenant_id, group_id, doc_acl, classification) and apply metadata filtering \/ ACL checks in the vector database so unauthorized chunks are never returned to the application.<\/li>\n<\/ul>\n
Detect<\/h4>\n\n- Flag \u201cdata fishing\u201d behavior: very broad searches, too many fetches, repeated similar queries.<\/li>\n
- Alert when the system returns a document that the user shouldn\u2019t be able to access.<\/li>\n<\/ul>\n
Respond<\/h4>\n\n- Rebuild the index after making changes to permissions or content.<\/li>\n
- Change the access keys if you suspect exposure.<\/li>\n
- Fix the document ingestion process so that permissions always carry over correctly.<\/li>\n
- Consider privacy-preserving methods when embedding highly sensitive enterprise datasets.<\/li>\n
- Implement access controls at the embedding level, not just the document level.<\/li>\n<\/ul>\n
How it works (example): The app sends the user\u2019s query, along with an authorization filter (such as group_id = Finance), to the vector DB. The DB searches only within that permitted scope and returns chunks that the user is allowed to access.<\/p>\n
Note: In most RAG implementations, security isn\u2019t applied to the embedding vectors themselves\u2014it\u2019s enforced during retrieval via metadata filtering and document-level authorization in the vector database, before the LLM ever sees the text.<\/p>\n
LLM09: Confidently wrong answers (Misinformation)<\/h3>\n
The model produces false but plausible outputs, and the business treats them as truth\u2014especially dangerous in HR, legal, finance, and security operations. OWASP describes misinformation as false\/misleading information that appears credible, potentially causing security breaches, reputational damage, and legal liability.<\/p>\n
![\"\"](\"https:\/\/www.solix.com\/blog\/wp-content\/uploads\/2026\/02\/quote-9-1024x244.png\" "\\"\\"")
<\/p>\n
Enterprise Security Controls<\/h3>\nPrevent<\/h4>\n\n- Use only trusted, approved company sources for answers. For legal, HR, security, or finance questions, require the bot to indicate where it obtained the answer.<\/li>\n
- When the bot is unsure, it should clearly state \u201cI don\u2019t know\u201d and request a person to review the answer before anyone acts on it.<\/li>\n
- Before releasing updates, test the bot with real workplace questions and do not launch unless it consistently answers correctly.<\/li>\n<\/ul>\n
Detect<\/h4>\n\n- Compare the bot\u2019s answers with the original documents it used; flag cases where the answer does not accurately reflect what the document states.<\/li>\n
- Provide users with an easy way to report incorrect answers, review those reports regularly, and address repeated mistakes as a serious issue that requires correction.<\/li>\n<\/ul>\n
Respond<\/h4>\n\n- Correct or replace the source document if it is outdated or wrong.<\/li>\n
- Update the bot\u2019s knowledge store so it uses the corrected content.<\/li>\n
- Adjust the bot\u2019s instructions and run the same tests again to confirm the problem is fixed.<\/li>\n<\/ul>\n
LLM10: Denial of service\u2014and denial of wallet (Unbounded Consumption)<\/h3>\n
LLMs can be exploited to consume excessive computational resources. Attackers (or even legitimate users) drive excessive inference: long prompts, repeated retries, heavy tool usage. The result is cost spikes, outages, degraded UX, or model extraction attempts. OWASP defines unbounded consumption as allowing excessive and uncontrolled inferences, which can lead to denial-of-service attacks, economic losses, model theft, runaway costs, or resource starvation for legitimate users, resulting in service degradation.<\/p>\n
![\"quote](\"https:\/\/www.solix.com\/blog\/wp-content\/uploads\/2026\/02\/quote-10-1024x261.png\" "\\"\\"")
<\/p>\n
Enterprise Security Controls<\/h3>\nPrevent<\/h4>\n\n- Set clear limits on the number of requests a person can make in a minute\/hour\/day.<\/li>\n
- Set a spending limit per person or per team, so one user can\u2019t run up the bill.<\/li>\n
- Set max token limits per request (cap input tokens, retrieved context tokens, and output tokens) so a single \u201cmonster prompt\u201d can\u2019t overwhelm the system.<\/li>\n
- Block overly large inputs and enforce hard timeouts for requests that run too long (to prevent long-form\/recursive DoS patterns).<\/li>\n
- Save and reuse common answers to avoid recomputing the same information repeatedly.<\/li>\n
- Control and slow down unusually heavy traffic before it reaches the AI system, especially for public-facing chatbots.<\/li>\n
- Limit recursion\/tool-call steps (max agent steps per request) to prevent runaway loops.<\/li>\n<\/ul>\n
Detect<\/h4>\n\n- Get alerts when daily or hourly costs suddenly jump.<\/li>\n
- Track what \u201cnormal\u201d usage looks like, and flag sudden increases in message size or request count.<\/li>\n
- Watch for sudden spikes in traffic or slowdowns that suggest overload or abuse.<\/li>\n<\/ul>\n
Respond<\/h4>\n\n- Immediately slow down or temporarily block the users or sources causing the spike.<\/li>\n
- Replace exposed access keys with new ones if you suspect misuse.<\/li>\n
- Temporarily switch off the most expensive features until usage returns to normal.<\/li>\n<\/ul>\n
Now that we\u2019ve covered the 10 failure modes, the real enterprise challenge is keeping controls effective as everything changes\u2014models get upgraded, prompts evolve, new data sources get indexed, and agents gain new tools. If you want GenAI initiatives to survive audits, leadership changes, and rapid rollout, you need governance that treats GenAI as a lifecycle-managed capability. NIST\u2019s Generative AI Profile (NIST AI 600-1), a companion to the AI RMF, lays out practical actions to govern, map, measure, and manage GenAI risks (setting clear ownership and rules, understanding how the system uses data, measuring risk with metrics and testing, and continuously improving controls over time).<\/p>\n
Think of OWASP as the \u201cwhat can go wrong\u201d taxonomy, and NIST AI RMF\/600-1 as the \u201chow to run the program\u201d scaffolding.<\/p>\n
GenAI Security Is a Lifecycle Program, Not a One-Time Test<\/h2>\n
Risk management for GenAI isn\u2019t a one-time \u201csecurity test before go-live\u201d exercise\u2014it has to follow the system across its entire lifecycle, because the risk profile keeps shifting as the system evolves. The moment you add a new RAG data source, connect an agent to a tool or workflow, fine-tune the model, tweak prompts and system instructions, upgrade model versions, onboard new user groups, or even change logging and retention settings, you\u2019ve effectively changed what the system can access, what it can reveal, and how it can be misused. That\u2019s why NIST emphasizes continuous risk management across the lifecycle, with practical actions organized under four functions: Govern<\/strong> (set accountability, policies, and decision rights), Map<\/strong> (understand the system\u2019s context, data flows, and exposure), Measure<\/strong> (test and track performance and risk with metrics and evaluations), and Manage<\/strong> (implement controls, monitor, respond to incidents, and continuously improve).<\/p>\n![\"GenAI](\"https:\/\/www.solix.com\/blog\/wp-content\/uploads\/2026\/02\/GenAI-Security-Lifecycle-Program-1024x527.png\" "\\"\\"")
<\/p>\n
A realistic 30\/60\/90 rollout plan<\/h3>\n
Here\u2019s a realistic way to move from \u201cwe launched a GenAI feature\u201d to \u201cwe run it safely at enterprise scale.\u201d This 30\/60\/90 plan prioritizes quick risk reduction first, then hardens controls where breaches actually happen, and finally proves resilience through testing, metrics, and repeatable governance\u2014without stalling delivery.<\/p>\n
Days 0\u201330: Stabilize (Visibility & Basic Guardrails)<\/h4>\n
Start with the basics that stop you from getting burned immediately:<\/p>\n