Test it
The assessment records each AI system you run, what it can reach and which published list of AI risks applies, without sending attack prompts. The tests then check the model, its output and its tools.
AI security services in India
Test and protect the AI in your product — so it can't be tricked.
We test your models, AI agents and the apps around them for the tricks that make AI leak data or take actions it shouldn't, such as prompt injection and loose tool permissions. Then we help you build the guardrails, logging and incident readiness that keep them fixed.
- Every finding shows what triggered it
- Retest of your fixes included
- We reply within one working day.
- OWASP LLM Top 10
- MITRE ATLAS
- CVSS v4.0
In brief
- What it is
- Security for the AI parts of your product: the model, the documents it looks up and the tools an agent can use. We test them, then help you guard them once they're live.
- Why it matters
- A language model can take orders from any text it reads, including a planted file. Once an agent can send email or change records, one hidden line can leak data or change things.
- What you get
- Findings with the prompt that triggered them, a CVSS v4.0 score and a fix. Then guardrails, logging and a retest that shows each fix held.
What we do
Test your AI, guard it, and govern it.
Everything we do for AI systems, in three groups. Not sure where to start? Start with the AI security assessment.
Guard it once it's live
We design the rules around your model and make sure your team would spot, and handle, anything that gets through.
Govern it
We help you list every AI system you run, weigh its risk and meet the standard or law that applies.
Building the AI itself? See AI development. For the web app, cloud and network underneath, see defensive cybersecurity.
Why AI is different
Why a standard security review misses AI risks.
A standard review assumes your application treats input as data. A language model treats everything it reads as possible instructions, including a document your retrieval pipeline (RAG) just fetched. So a planted file can steer the model without tripping a web application firewall.
The risk grows once an agent can act. If it can query a database, update a CRM record or send email, one injected instruction can become leaked data, a changed record or a financial loss.
The four layers we look at
- Model and prompt. Direct and indirect prompt injection, jailbreaks (tricks that talk a model out of its rules) and system prompt extraction. OWASP LLM01 and LLM08.
- Retrieval. One customer's documents leaking to another, poisoned embeddings and index permission bypass. OWASP LLM02 and LLM09.
- Tools and agents. Excessive agency, unchecked tool parameters and unintended changes to data. OWASP LLM03.
- The web app and cloud APIs underneath. Login, tenant isolation, input handling and rate limits go through vulnerability management and secure code review. A clean model test says nothing about the login form.
Because we build AI systems too, we test yours the way they're wired in production.
Defensive AI security
Guardrails keep your AI inside the limits you set.
Testing finds the gaps. Guardrails keep them closed once the system is live.
A guardrail is a check between the model and everything else. It looks at what goes in, what comes out and what the model tries to do. Anything outside the rules is blocked or waits for a person, like the spending limit on a company card.
What we design and review
- Input and output checks. Prompt-injection filters, redaction of personal data, and rules for the links and markup the model writes.
- Tool limits. Each tool cut to what the task needs, with a person approving any action that sends, pays or deletes.
- Retrieval by the user's own rights. The model sees only what the signed-in user could open, not everything the index can reach.
- Your current setup. An AI security posture review of model provider settings, API keys and their scopes, logging and data retention.
We then verify they hold against the prompt-injection and jailbreak cases that matter in your deployment.
Logging and incident readiness
Spot trouble early, and know who acts.
We build it into the detection and incident response you already run.
- Logs where your team already looks. Prompts, tool calls and guardrail decisions, next to your other security logs. See logging and detection for AI systems.
- Alerts worth a person's time. Repeated injection attempts, blocked tool calls or a sudden jump in usage.
- An AI incident runbook. How to switch off a tool, rotate a key or pull a document from the index, and who decides, inside your incident response plan.
- Checks after every change. Guardrails are checked again after a model, prompt or tool change, so a fix stays verified.
What this work does not include. Each engagement covers the build you give us when we test it; a clean result describes only that build. We set up logging and alerts, but SecWiz does not operate a SOC (security operations centre): your team, or a provider you choose, runs them day to day. For ongoing help, see managed security and vCISO. We don't run denial-of-service tests. We are not a CERT-In empanelled auditing organization, and a test from us is not an audit.
Common failures
Six ways AI features go wrong.
Each maps to an entry in the OWASP Top 10 for LLM Applications, 2026 edition.
A planted document takes over (prompt injection, LLM01)
A retrieved document, supplier invoice or customer ticket carries planted instructions that override the system's rules and send private data out. See prompt injection testing.
An agent can do too much (excessive agency, LLM03)
An agent has write access to a database, or a payment tool, with no person approving its actions, so an attacker can trigger changes nobody authorized. See the agent tool-permission review.
Search returns other customers' files (retrieval permission bypass, LLM02)
The app's screens check file access, but the vector search index behind the model (pgvector or Qdrant, say) returns chunks of other tenants' private documents. See RAG security testing.
The model's answer becomes live code (improper output handling, LLM10)
Raw model output is shown as HTML or placed into SQL queries unsanitized, so made-up text becomes a cross-site scripting (XSS) or injection flaw. See output handling review.
The model reveals its hidden instructions (hidden context exposure, LLM08)
Business logic, unreleased feature flags and API keys in the system instructions are coaxed out through conversational jailbreaks. See prompt hardening.
Runaway usage runs up the bill (unbounded consumption, LLM06)
Uncapped context windows and unmetered, looping tool calls let one malicious user run up thousands of dollars in cloud LLM API charges within minutes. See how we test unbounded consumption.
How we work
A report your developers can act on.
It carries the evidence an auditor usually asks to see.
Scoping
A short call on the three things that set the scope. You get a proposed scope and schedule before testing starts.
Findings with evidence
Each finding carries the prompt or request that triggered it, what came back, a CVSS v4.0 score, the MITRE ATLAS technique where one applies, and the fix. Scanner output alone doesn't count.
A plain executive summary
It says which findings don't matter in your deployment, so engineers don't spend time on issues nobody can reach.
A retest of your fixes
After your fixes ship, we test the same findings again and record which are closed and which are still open.
FAQ
Common questions about AI security services.
Testing, guardrails, timing and independence. For anything else, ask us directly.
AI security is the work of finding and fixing the ways an AI system can be manipulated, made to leak data or pushed to act beyond its remit, and of keeping it protected once it's live. The failure modes differ from ordinary application security. A language model treats retrieved text as instructions, and an agent with a tool can take a real action in a real system.
AI security testing adds checks a standard review doesn't have, for the model, the retrieval index and the tools an agent can call: prompt-injection resilience, jailbreak and guardrail checks, data-leakage checks and a review of agent tool permissions. The ordinary web application underneath is assessed separately, through vulnerability management or secure code review. Every finding comes with the prompt or request that triggered it and a fix.
LLM security testing covers the language model and what sits in front of it, worked through against the OWASP Top 10 for LLM Applications, 2026 edition. Some entries can be tested with only a login and a chat box. The rest need access you arrange, and our LLM security testing page lists which is which.
The work that keeps an AI system safe once it's live: guardrail design, an AI security posture and configuration review, logging and monitoring for prompts, tool calls and guardrail decisions, and incident-response readiness for AI failures. We build it into the detection and incident response you already run rather than beside them.
Yes. We test an agent's tool scope and permission boundaries, what it does when retrieved content carries instructions, and how far the damage spreads when it gets something wrong. The agent tool-permission review is scoped as part of LLM security testing.
It depends on the scope more than on the type of engagement. Three things set it: how many of your systems the model can reach, whether we can test against the system prompt and the retrieval corpus, and whether agents and their tools are in scope. Send us those three details. We reply within one working day, and a proposed scope and schedule follow a short scoping call.
Usually, yes. A hosted model doesn't remove your risk. You still decide what data reaches it, what its output is allowed to do and which of your systems it can reach. Most of the findings we raise are in your own code.
Yes, but we staff the test with people who didn't build it, and the report says so. If the test is for compliance and independence is the point, a second opinion from another firm is the better choice, and we'll tell you that.
Standards we reference
- OWASP GenAI Security Project, OWASP Top 10 for LLM Applications, 2026 edition.
- MITRE, MITRE ATLAS, Adversarial Threat Landscape for Artificial-Intelligence Systems.
- FIRST, CVSS v4.0, Common Vulnerability Scoring System version 4.0.
Last updated 25 September 2026.
Let's talk
Let's make your AI hard to trick.
Tell us what the model can reach, whether agents call tools, and when you plan to launch. After a short scoping call you get a proposed scope and schedule. We reply within one working day.