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SIRI AI Security — securing what you're building with AI.
LLMs, RAG pipelines and autonomous agents are becoming digital actors inside your business — making decisions, touching data, taking actions. Most of them were never adversarially tested. SIRI AI Security tests them the way they'll actually be attacked.
78%Of organisations have deployed AI in production
12%Of those have a formal AI security testing programme
97%Of enterprises have had at least one AI-related security incident
Why AI needs a different kind of testing
Live tracking · scroll to see what's driving the exposure
Adoption vs. testing
78% / 12%
78% of organisations have deployed AI in production, but only 12% have a formal AI security testing programme in place (2026 enterprise AI security benchmarks).
Shadow AI
78%
78% of AI users at work bring their own AI tools outside IT approval (Microsoft Work Trend Index, 2025) — meaning exposure often exists whether or not AI use is officially sanctioned.
Data exposure
27%
27% of enterprise employees have entered confidential company data into public AI tools, including customer records and financial information (Salesforce, 2024).
Visibility gap
60%
60% of security teams say they lack visibility into which AI tools their workforce is actually using (Cisco AI Readiness Index, 2024).
Standardising
OWASP & NIST
The OWASP Top 10 for LLM Applications (2025) and the NIST AI Risk Management Framework are becoming the common reference points for scoping and reporting AI security work, alongside ISO/IEC 42001.
The systems becoming digital actors in your business need a different kind of testing
Traditional application security wasn't built to test a system that reasons, retrieves and acts.
An LLM-powered feature isn't just another API endpoint. It can be manipulated through the content it processes, not just the requests sent to it; it can leak what it was never meant to disclose; and if it's wired to take actions — an agent that can send emails, query a database, or call other systems — a successful manipulation doesn't just return bad data, it does something.
Most organisations that have shipped AI features tested the model's accuracy and the application's functionality, but never adversarially tested the AI system itself — prompt injection (direct and indirect), jailbreaks, training-data and RAG-pipeline poisoning, excessive agency in autonomous workflows, and model or supply-chain integrity. That gap is precisely where 97% of enterprises report having already had at least one AI-related security incident.
Only 12% of organisations with AI in production have a formal AI security testing programme
Against 78% adoption of AI in production — a wide and growing gap between how fast AI is being shipped and how rigorously it's being tested. (2026 enterprise AI security benchmarks.)
SIRI AI Security tests the full pipeline — model, retrieval layer, plugins, agent actions and the data stores behind them — under realistic adversarial conditions, and reports findings mapped to the OWASP Top 10 for LLM Applications and the NIST AI Risk Management Framework, so results are legible to both engineering and the board.
What organisations get wrong
Four assumptions that leave AI systems untested
Most AI security gaps aren't about a lack of concern — they're about not knowing what to test for yet.
01 — OWNERSHIP
“Our AI vendor handles security”
A foundation-model provider secures the model weights; it doesn't secure how you've wired that model into your own data, plugins and business logic.
02 — SCOPE
“We only need to secure the model”
The RAG pipeline, plugins, agent actions and connected data stores are all part of the attack surface — securing the model alone leaves the rest untested.
03 — SEVERITY
“Prompt injection is a minor issue”
Where an AI system is wired to take actions, a successful prompt injection doesn't just return bad text — it can trigger a real action with real consequences.
04 — VISIBILITY
“We don't officially use AI, so we're not exposed”
78% of AI users bring their own AI tools outside IT approval — shadow AI creates exposure whether or not AI use is formally sanctioned.
What SIRI AI Security covers
Testing across the full AI pipeline, not just the model
From the model to the retrieval layer to the actions an agent can take — mapped to the OWASP LLM Top 10 and NIST AI RMF throughout.
LLM SECURITY
Adversarial LLM Testing
Adversarial testing of large language models for prompt injection, jailbreaks and data leakage.
- Direct & indirect prompt injection
- Jailbreak & guardrail-bypass testing
- Sensitive-data leakage testing
RAG SECURITY
Retrieval & Knowledge-Base Security
Security testing of retrieval-augmented generation pipelines and the knowledge bases behind them.
- Retrieval-pipeline testing
- Knowledge-base poisoning checks
- Data-source access controls
AGENT SECURITY
AI Agent & Autonomous Workflow Security
Security testing for autonomous and semi-autonomous AI agents that can take real actions.
- Excessive-agency testing
- Tool & plugin permission review
- Multi-step action-chain testing
MODEL SECURITY
Model Integrity & Supply Chain
Model integrity, supply-chain and inference-time security assessment.
- Model provenance & supply chain
- Inference-time abuse testing
- Model-theft & extraction risk
AI RED TEAMING
Adversarial Testing Under Realistic Conditions
Full adversarial testing of AI systems under realistic, sustained attack conditions.
- Objective-based AI red teaming
- Mapped to OWASP LLM Top 10
- Mapped to NIST AI RMF
ARCHITECTURE
AI Security Architecture
Secure-by-design architecture guidance for AI-powered products, before and after launch.
- Secure-by-design review
- Guardrail & policy design
- ISO/IEC 42001 alignment
Evidence, not guesswork
No AI security programme vs. generic AppSec vs. SIRI AI Security — what actually differs
Application security testing and AI-specific adversarial testing find different things.
| Approach | No AI security programme | Generic AppSec applied to AI | SIRI AI Security |
|---|---|---|---|
| Prompt injection & jailbreak testing | No | Rare | Included |
| RAG / knowledge-base pipeline coverage | No | No | Included |
| Agentic / autonomous workflow testing | No | No | Included |
| Mapped to OWASP LLM Top 10 / NIST AI RMF | No | Rare | Yes |
| Shadow-AI / unsanctioned tool visibility | No | No | Assessed as part of engagement |
Sources: OWASP Top 10 for LLM Applications (2025); NIST AI Risk Management Framework; Microsoft Work Trend Index 2025; Salesforce 2024; Cisco AI Readiness Index 2024. Summarised for comparison.
Numbers every board should know
What the adoption-vs-testing gap actually looks like
78%
Have AI in production
Of organisations have deployed AI-powered features into production systems.
12%
Have formal testing
Of those organisations have a formal AI security testing programme in place.
97%
Had an AI incident
Of enterprises report having encountered at least one AI-related security incident.
27%
Pasted confidential data
Of employees have entered confidential company data into public AI tools (Salesforce, 2024).
Why SIRI for AI security specifically
Built for emerging technology from the start, not bolted on afterward
AI security isn't a checkbox added to an existing AppSec programme — it's a distinct discipline SIRI is built around.
01
Built for emerging technology
AI, autonomous systems and connected infrastructure are core to how SIRI is built, not an afterthought bolted onto a traditional security practice.
02
Full-pipeline coverage
Testing covers the model, retrieval layer, plugins, agent actions and connected data stores — not just the model in isolation.
03
Mapped to current frameworks
Findings are reported against the OWASP Top 10 for LLM Applications, the NIST AI RMF and ISO/IEC 42001, so results are legible to engineering and the board alike.
04
Connected to the rest of SIRI Security
AI findings escalate directly into SIRI Attack for broader offensive validation and SIRI MDR for ongoing monitoring — not a standalone report.
Who this is built for
Organisations SIRI AI Security is built for
AI Companies
Technology & SaaS
Financial Services
Healthcare
Any organisation deploying LLM features
Enterprises facing AI-usage questionnaires
How we work
From mapping the AI attack surface to continuous red teaming
01
Map the AI Attack Surface
Inventorying every model, RAG pipeline, plugin and agent action in scope.
Week 102
Adversarial Testing
Prompt injection, jailbreaks, data exfiltration and agentic-action testing.
Weeks 2–303
Harden & Remediate
Guardrail, architecture and policy recommendations mapped to findings.
Week 404
Continuous AI Red Teaming
Ongoing adversarial testing as models, prompts and agent capabilities change.
OngoingFrequently asked
SIRI AI Security, answered directly
How is this different from a normal penetration test?
A conventional penetration test targets infrastructure and application logic. AI security testing specifically targets how a model, its retrieval pipeline and any connected agent actions can be manipulated through content and context, which conventional AppSec tooling isn't built to find.
Do you test third-party models like GPT or Claude, or only custom-built systems?
Both. Where you've built on top of a third-party foundation model, testing focuses on how it's integrated — the prompts, RAG pipeline, plugins and permissions you control — since the underlying model weights are the provider's responsibility.
Can you test autonomous or agentic AI systems specifically?
Yes — agentic security is a core part of the service, testing whether an agent can be manipulated into taking actions outside its intended scope, and whether its permissions are appropriately constrained.
Can you help with a shadow-AI policy, not just testing?
Yes, where scoped — visibility into unsanctioned AI tool use is often a starting point for a broader AI governance and policy programme, which SIRI AI Security can support alongside technical testing.
What frameworks are results mapped to?
Findings are reported against the OWASP Top 10 for LLM Applications (2025) and the NIST AI Risk Management Framework, with alignment to ISO/IEC 42001 where an AI management-system programme is in scope.
Test what you've actually shipped
Find out how your AI systems fail under real attack.
Start with a scoped assessment, or move to continuous AI red teaming if you're shipping fast.
24/7 for active incidents: +91 79819 12046
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