Prompt-injection defenses
Define the task and acceptable result for Prompt-injection defenses before choosing models, prompts or supporting data.
AI & Intelligent Systems
AI Security
Gromnii designs security controls for models, agents, retrieval and AI-enabled applications.
Requirement
Context
Reason
Tools
Control
Outcome
When this is useful
Use AI security when models, retrieval systems or agents can access sensitive information, external content or business tools. Controls need to address prompt injection, data leakage, tool abuse, model access and unsafe fallbacks in addition to ordinary application security.
What Gromnii builds
Agent permission controls
Create, change and remove Agent permission controls through an owned lifecycle tied to the identity source.
Data leakage controls
Reduce unintended disclosure by controlling prompt context, retrieval scope, tool outputs, logs and model-provider data handling according to sensitivity.
Secure RAG
Measure Secure RAG against task-specific quality, latency and cost limits rather than one generic score.
AI red-team testing
Test prompt injection, data extraction, unsafe tool use, privilege bypass and adversarial inputs against the actual application boundaries rather than only the base model.
How the AI system is controlled
This reference shows one possible AI Security arrangement. The actual design depends on the systems, constraints and controls involved.
01Identity
02Input boundary
03Model / agent
04Tools & data
05Output controls
06Monitoring
What matters in production
Least privilege
Give models, agents and tools only the data scopes and actions required for the task, and separate read, write, approval and administrative authority.
Tool restrictions
Treat tool restrictions as a measurable operating condition for AI Security, with explicit thresholds, ownership and a defined response when the condition is not met.
Data boundaries
Define which data may enter prompts, retrieval stores, model providers and logs, and enforce those boundaries separately for users, agents and automated tools.
Fallback behavior
Define what the application does when the model is unavailable, unsafe, low confidence or denied a required tool, including when to stop rather than improvise.
What it can improve
Tighter AI permissions
Limit which data, tools and actions each AI workload may use instead of relying on prompts as the security boundary.
Lower data-exposure risk
Apply retrieval filters, output controls, secret handling and logging to reduce accidental or manipulated disclosure.
Stronger attack testing
Test prompt injection, indirect instructions, tool misuse and unsafe failure behavior before and after deployment.
Additional technical detail
Technical implementation notes for AI Security.
Show additional technical detail
Controls for models, tools, data, and agents
The threat model changes with model access, tool permissions, untrusted content, sensitive data, and the consequences of generated actions.
01Prompt injection and tool-access protections
Reduce the chance that untrusted instructions can override application rules or reach tools they should not control.
02Agent permission and data leakage controls
Limit what agents can read, send, store, and change across sensitive data and business systems.
03Secure RAG and model access controls
Enforce identity and source permissions through retrieval and model access rather than exposing a shared knowledge pool.
04AI application security architecture
Treat models, prompts, tools, APIs, identities, data, and logs as one application security boundary.
05AI red-teaming and security review
Probe realistic misuse, prompt attacks, data exposure paths, tool abuse, and unsafe edge cases before release.
Secure AI through explicit boundaries
AI security follows the path from user input and retrieved context through models, tools, data, outputs, logs, and downstream actions.
Identity
Prompt / Context
Tools / Agents
Data
Controlled
technology
technology
Protect the AI interaction surface
AI controls are useful when they can be tested against realistic misuse and observed after release.
Abuse and misuse resistance
Reduce the risk of adversarial prompts and unauthorized tool use.
Data protection
Limit what models and agents can retrieve or emit.
Aligned with cybersecurity
Connect AI-specific controls to broader identity, application, and cloud security.
Related solutions and industries
Discuss a Project
Describe what AI Security should change, the systems it must work with and the constraints that matter.