AI Security
Secure AI systems before they become a security problem. We build the identity checks, minimum-access permissions, threat models, and red teaming that protect production AI agents and LLM applications, mapped to the OWASP GenAI Top 10. So an agent's action surface stops being your attack surface.
Testing how well the model performs is part of AI Evaluation. Watching it in production is part of AI Operations. This is the threat model and the controls, not the score or the dashboard.
Tested Against Real Attacks, Not Assumed Safe
Securing the app around the model isn't the same as securing the model. We build minimum-access permissions, threat models, and red teaming mapped to the OWASP GenAI Top 10, so an agent's action surface stops being your attack surface.
What is AI security?
AI security is protecting AI agents and LLM applications from misuse, data leaks, and manipulation. It covers who and what the system can act as, what it's allowed to do, threat modeling, red teaming, and review of the models and plugins it depends on, so the system's own tool access and independence don't become an attack surface. It's also called LLM security or AI agent security.
Tools We Use
Mapped to standards, backed by real controls: we use the OWASP LLM Top 10 as the threat model, with secrets management and logging tools to enforce it and prove it.
Recognize the symptoms
When You Need AI Security
If two or more of these are already true, this isn't a tuning problem.
- Agents carry more tool permissions than they need by default
- Prompt injection can reach sensitive tools
- It's unclear where the data boundaries are between users, customers, and agents
Securing the app around the model isn't the same as securing the model.
- AI security covers the controls that stop AI applications, agents, models, and the data they touch from being misused, leaked, manipulated, or tricked into unsafe action.
- AI and agent systems open up ways to attack a system that standard application security doesn't cover: tricking the model through its input or the content it reads, misusing its tools, letting it do more than it should, and weaknesses in how it searches its own data.
- We map the system's risks against recognized industry standards, make sure every agent only has the access it needs, and test for prompt injection and tool misuse directly on the live system.
- We protect passwords, keys, and what the system remembers, and produce evidence your customers' security teams can actually use, not a safety filter installed and never tested.
What We Build
Eight parts that work together as one security setup, organized around what actually stops an incident, not which guardrail library installed it.
Identity
Separate identity checks for the person, the agent, and the tool it calls, so a session can't be hijacked or an identity faked just by typing the right words into a prompt.
Access rules
Access rules based on information the server controls, like who the user is and which account they're in, never taken from text the user can type and manipulate.
Minimum access
Every agent gets only the tools and data it needs for its job, with a named owner and a risk rating before it goes live.
Tool permissions
Tools that only look things up are kept separate from tools that change things. Anything destructive or outward-facing needs approval by default, and every high-impact action is logged with what it did, who approved it, and when.
Threat modeling for LLMs
A threat map built for your specific system, using the OWASP GenAI Top 10 and MITRE ATLAS frameworks, covering prompt injection, supply chain risk, poisoned data, and an agent doing more than it should.
Safety filters and limits
Safety checks at every stage (what goes in, what gets retrieved, what runs, what comes out) that block or flag problems in real time instead of just logging them, plus limits that stop cost blowouts and attacks meant to run up your bill.
AI red teaming
We actually try to break it: prompt injection against its instructions, misusing its tools, and feeding it poisoned data. AI red teaming and LLM penetration testing run against the real, live system, not a checklist.
AI security testing
A test suite that attacks the system in every way the OWASP GenAI Top 10 describes. It's saved and rerun every time a prompt, model, tool, or safety filter changes.
Adversarial testing against a live system has its own page: AI Red Teaming. Testing how well the model performs, designing benchmarks, and catching quality regressions are part of AI Evaluation. Production monitoring and incident runbooks are part of AI Operations.
What Good AI Security Needs
Eight parts that work together, not a safety filter installed and assumed to work.
Risk check against the OWASP list
A full check against the OWASP LLM Top 10, the industry-standard list of the ten biggest AI security risks, not a partial scan.
Permission map
A clear map of which agents can do what: send email, update customer records, see personal data, run code, or spend money.
Minimum access for tools
Tools that only look things up are kept separate from tools that change or send things, and anything destructive or outward-facing needs approval first.
Prompt injection testing
Tests for prompt injection, which is trying to trick the model into ignoring its instructions, run against both what a user types and what the model reads from other sources.
Protecting passwords and keys
Passwords and API keys kept out of everything the model reads, remembers, or logs, with access limited and keys changed regularly.
Keeping customers' data apart
Search kept locked to each customer's own data, tested to make sure one customer's data can't show up in another's results.
Safety filter testing
Safety filters tested for whether they can be tricked, whether they block things they shouldn't, and whether they miss things they should catch.
Evidence and reporting
Every issue logged with what was tested, what should have happened, and what actually happened, tied to the standards your customers' procurement teams ask for.
Systems We Secure
Organized around the real system, not the attack technique.
Customer-facing support agents
Agents with access to account data and refund or update actions, where a tricked model becomes a real financial or privacy incident.
Internal agents that can change data
Agents that update internal systems on their own, tested for whether their permissions match what their job actually needs.
Document search systems (RAG)
Systems where a malicious instruction can hide inside a retrieved document, tested against content the model reads, not just what a user types.
AI features shared across customers
Tested specifically for one customer's data leaking into another's search results or memory.
Regulated and compliance-heavy systems
Systems that need evidence for SOC 2, ISO 42001, or the EU AI Act, tested and documented to the standard your auditors and customers expect.
How the Controls Are Structured
Not a safety filter bolted on after the fact.
Layers that back each other up
Minimum access, safety filters, and monitoring stacked as independent layers, so one control failing isn't enough to cause an incident.
Two-model split for prompt injection
One model holds the tools but never reads untrusted content. A second model reads untrusted content but can't take action. That breaks the path an injected instruction needs to reach a tool.
A gatekeeper between agent and tools
A layer between the agent and its tools that enforces what's allowed using rules on the server, never taking permission from text the model or a user can manipulate.
Checks in the request path
Checks that sit in the request path itself and can block or flag an action in real time, not a filter that only logs a problem after the fact.
What You Receive
AI Security vs Traditional Application Security
AI and agent systems add ways to attack a system that traditional application security doesn't cover: tricking the model through its input or the content it reads, misusing its tools, letting it do more than it should, poisoned training data, leaking its own instructions, and weaknesses in how it searches its own data. We test against the OWASP LLM Top 10 and related standards on top of standard access control and password and key handling, not instead of it.
AI Security vs AI Governance
AI Governance builds the boundaries: the inventory, the risk scoring, the approval steps, and the permission rules. AI Security tests whether those boundaries actually hold, by trying to break in and seeing if an attacker can trick an agent into bypassing a permission check.
Ten risks, mapped to the OWASP GenAI Top 10.
The 2025 edition of the OWASP Top 10 for LLM Applications, and what our AI security testing checks for under each one.
Prompt injection
Instructions that override the system's own, typed directly by a user or hidden in content the model reads elsewhere. It's been the OWASP GenAI Top 10's #1 risk two editions running, and it's the first thing our AI red teaming targets.
Sensitive information disclosure
The model exposing personal data, credentials, or proprietary data it should never show. It's the fastest-rising risk in the 2025 OWASP ranking, and our data leakage testing is built to catch it.
Supply chain risk
Vulnerabilities in third-party models, fine-tunes, plugins, and training data you didn't build. We check them instead of assuming they're safe because a vendor shipped them.
Data and model poisoning
Training or fine-tuning data manipulated to change how the model behaves later, in ways that don't show up until the behavior triggers. Covered in our model security review.
Improper output handling
Model output that's trusted and run, displayed, or passed downstream without checking. It's the path from a chatbot reply to code execution or an injection flaw elsewhere in your stack.
Excessive agency
An agent with more independence, tools, or permissions than its task needs. Our minimum-access work exists to close this before it becomes an incident.
System prompt leakage
Instructions or secrets in the system prompt pulled out through the right sequence of questions. We test it alongside prompt injection and never assume it stays hidden.
Vector and embedding weaknesses
Weak access control or contamination in the search layer that lets one customer's data leak into another's results. Tested as part of our RAG and memory security work.
Misinformation
The model stating something false with full confidence. It's a quality problem that becomes a security problem the moment that output drives an action or a decision.
Unbounded consumption
No limits on cost, request volume, or resource use per user or session. It's the gap that turns one bad actor into a runaway bill or an outage.
From our experts.
Writing on agent risk, safe rollout, and production failure modes.
Our AI Security Process
The same nine-phase process we use everywhere, with a checkpoint at every step. For a security engagement, testing means trying to break the system, trying to get past its safety filters, and scanning for exposed secrets, well beyond a normal functional review.
Related Engineering Services
Common questions.
Bring us the agent nobody's stress-tested yet.
Whether it's a prototype with broad access and no permission rules, or a live agent that's never been tested against someone trying to trick it, we'll look at where it stands before recommending anything.
No hype · No forced roadmap · Just a clear view of what the system needs next