Security for systems
that think.
We secure AI applications — assistants, copilots, agents and retrieval pipelines — and the data layers beneath them. Red teaming, guardrails, architecture review and monitoring, delivered by engineers who run data platforms in production.
AI changed the attack surface.
An LLM application does not fail the way a web application fails. It takes instructions from everywhere — user input, retrieved documents, tool results, web pages — and it acts with the permissions you gave it. Classic security still matters. It is just no longer enough.
Three layers, one system.
We secure the intelligence, the autonomy and the data — because in a modern AI system they are inseparable.
LLM Applications
Assistants, copilots and RAG search. We test and harden the full path: prompt construction, context assembly, retrieval and output handling.
Explore →02AI Agents
Tool calls, integrations and autonomous workflows. Least privilege, sandboxing, identity and audit trails for software that acts.
Explore →03The Data Layer
Databases, pipelines and corpora that feed AI. Encryption, access control, provenance — the foundation under every trustworthy model.
Explore →What we do.
Focused on LLM security, backed by data security — every engagement ends with findings fixed and retested, not a PDF on a shelf.
AI Red Teaming
Adversarial testing of your LLM application: jailbreaks, direct and indirect prompt injection, data exfiltration, tool abuse — turned into a prioritized fix list.
Learn more →02LLM Security Review
Architecture and code review of the full AI path: context assembly, prompts, retrieval, tool interfaces and output handling.
Learn more →03Guardrails Architecture
Input and output filtering, content policies, rate limits and human approval — layered so one bypass never becomes a breach.
Learn more →04Agentic AI Security
Least-privilege tool design, sandboxing, identity and audit trails for autonomous agents. Bound what an action can cost.
Learn more →05AI Governance & Compliance
Readiness for the EU AI Act, ISO/IEC 42001 and NIST AI RMF: classification, policies, documentation and evidence.
Learn more →06Data Security Engineering
Encryption, access control, audit and hardening for the databases and pipelines your AI runs on.
Learn more →Born in data engineering.
Focused on AI.
DoThinkSecure grew out of DoThinkData — a practice that designs and runs PostgreSQL platforms, CDC pipelines and staging environments in production.
When clients began wiring LLMs into the platforms we built, we kept finding the same gaps: prompts assembled from untrusted content, agents holding database credentials, retrieval corpora that mixed access levels. We opened DoThinkSecure to close those gaps — with the discipline of data engineering applied to the new intelligence layer.
A model with access to your data inherits every risk of that data — and adds new ones. Security for AI is data security, continued.
Assess. Fortify. Monitor.
One honest loop, the same for every engagement.
Assess
We build a threat model of your AI system, then attack it: red teaming against the OWASP LLM and agentic risk families. Findings arrive prioritized, in plain language, with fixes.
Fortify
Guardrails, architecture changes, least-privilege tool design, output validation. Reviewable, reversible, documented — no security theater.
Monitor
Continuous test suites that re-run on every release, watching for injection regressions, data leakage and drift. Security that stays true after launch.
Shipping AI?
Know your exposure.
Tell us what you are building — model, data, agents — and we will scope an assessment. The first call is free.
What you get
- A threat model of your AI system
- Prioritized findings with fixes, not just findings
- A retest that proves the fixes work