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AI & LLM Security Services

Protect Models, Prompts, and Agents from the Attacks Security Tools Miss

AI is the #1 investment priority for security leaders — and the fastest way to leak source code, customer data, and credentials. Saral Cyber Team red-teams your LLMs, locks down RAG pipelines, and puts guardrails around copilots before they hit production.

OWASP

LLM Top 10 Mapped

2 wks

Red Team Sprint

0-trust

Agent Tooling

Get Free Consultation → Talk to Saral Cyber Team

What Saral Cyber Team Delivers

Integrated outcomes — monitoring, response, and proof — not a pile of disconnected tools.

LLM Red Teaming

Adversarial prompts, jailbreaks, indirect injection via docs, and tool-abuse tests against your actual chatbot — not a generic lab model.

Prompt Injection Defense

Input filters, untrusted-content isolation, and output allowlists so retrieved PDFs cannot hijack the system prompt.

RAG & Data Leak Controls

We test whether the bot will recite salary sheets, source code, or PII from your vector store — then close the retrieval path.

Agent & Tool Security

Least-privilege tool grants, human-in-the-loop for irreversible actions, and audit logs for every function call.

Model Supply Chain

Fine-tune data provenance, third-party model cards, plugin review, and shadow-AI discovery across SaaS copilots.

EU AI Act & Policy

Risk classification, logging, human oversight, and acceptable-use policy that legal and engineering can both sign.

Case Studies & Outcomes

What changed when teams stopped buying isolated products and started buying a cyber program.

Case Study

SaaS Copilot: Stopped Source-Code Exfiltration

Problem: A product copilot could be tricked into dumping internal API keys hidden in retrieved Confluence pages.

Solution: Indirect prompt-injection suite, retrieval allowlists, and a canary-token in docs. Guardrails deployed in the orchestration layer, not just the UI.

Result: Zero successful exfil in retest. Copilot launched on schedule with a security sign-off the board accepted.

0

Exfil on Retest

12 days

To Sign-off
"They attacked the bot the way a motivated intern would — that is what we needed."— Head of Product — Bengaluru
Case Study

Health AI: PHI Leak in RAG Closed

Problem: A clinical summarizer retrieved neighbouring patient notes when prompts were slightly rephrased.

Solution: Chunk-level ACLs, evaluation harness with PHI canaries, and output DLP on the completion stream.

Result: Cross-patient leakage dropped to zero in eval. DPDP mapping documented for the hospital's DPO.

0

Cross-patient Leaks

DPDP

Ready Pack
"The red team found the leak our model eval never scored."— CIO — Chennai

How We Engage

A repeatable path from coverage map to measurable risk reduction.

1

AI Asset Inventory

Map models, copilots, RAG stores, plugins, and shadow AI in SaaS. You cannot defend what is unofficial.

2

Adversarial Test

OWASP LLM Top 10 plus your domain abuse cases. We weaponize documents, not just chat boxes.

3

Guardrails in the Path

Fixes live in orchestration: isolation, allowlists, DLP, and tool permissions — not a 'please don't jailbreak' system prompt.

4

Continuous Eval

Regression prompts in CI. New datasets and agents do not ship without the harness going green.

Why Saral Cyber Team

Enterprise-grade security, built for the mid-market teams who actually have to run it.

We Test Your Bot, Not a Demo

Engagements run against staging with your retrieval data. Generic jailbreak lists are a starting point, not the deliverable.

Security + Product, Together

Fixes are designed so UX still works. We do not 'secure' the copilot by turning it into a brick.

Regulations Included

EU AI Act, DPDP, and ISO 42001 language is in the report so legal is not translating from a pentest PDF.

Engineering-Native Output

Tickets with repro prompts, traces, and suggested orchestration diffs — not a 80-page threat novel.

Client Testimonials

Security leaders, IT owners, and operators we sit with in the war room.

★★★★★

"They treated prompt injection like a real vuln class. Our previous vendor just ran a chatbot questionnaire."

Nisha R.
Nisha R.ML Lead — Gurugram
★★★★★

"Shadow AI inventory alone paid for the sprint. Finance had three unsanctioned copilots on customer data."

Dev P.
Dev P.CTO — Pune
★★★★★

"Board asked about AI risk. We finally had a mapped control set instead of a shrug."

Rahul S.
Rahul S.CISO — Noida

Ready to close this gap?

Book a free consultation with Saral Cyber Team. No product dump — a coverage map and the first controls that pay off.

Schedule Free Consultation → 📞 +91 79883 75156

Expert Insights

Practical notes from the people who run these programs.

Prompt Injection

Indirect Prompt Injection via RAG Is the Breach Path Nobody Scans

A poisoned PDF is enough. How we test retrieval-augmented generation in production-like staging.

Agents

Give an LLM a Tool and It Becomes an Insider

Least privilege for function calling, and why 'the model is aligned' is not an access-control strategy.

EU AI Act

A Practical AI Act Gap Assessment for Indian SaaS Exporters

What to log, what to disclose, and what can wait if you are not placing a high-risk system.

Frequently Asked Questions

What is included in an AI red team sprint? + A 2-week engagement: asset inventory, OWASP LLM Top 10 tests, indirect injection via your documents, tool-abuse cases, and a fix workshop with engineering. You leave with repro prompts and a guardrail backlog.
Can you secure Microsoft Copilot / ChatGPT Enterprise / custom RAG? + Yes. We cover SaaS copilots, API-hosted models, and custom orchestration (LangChain, LlamaIndex, in-house). Controls differ; the abuse cases do not.
Will this slow our AI roadmap? + The sprint is designed to unblock launch. We prioritize leak and tool-abuse issues that would stop a release, then leave a CI harness so later features do not regress.
Do you train our models? + We do not fine-tune production models as the core service. We secure the system around them — data, prompts, tools, logging — and review training-data provenance when you fine-tune.
How does this relate to the EU AI Act? + We classify the system, map transparency, logging, and human-oversight gaps, and hand legal a control matrix. It is not a law-firm opinion; it is the engineering evidence they need.
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