We red-team your AI before the internet does.
We break, secure and govern the systems and the AI you run: applications, cloud, identity, models, agents and their tools. Then we stay until the fix is shipped, monitored and proven to an auditor.
Offensive Security · AI Security · Security Advisory · Risk & Compliance
Engagement · Web & API testSimulated environment, real workflow.
Engagement · Support assistantSimulated environment, real workflow.
ops.example.com/cases/CASE-8812
Simulated environment, real workflow.
app.resilientprivacy.com/overviewSimulated environment, real workflow.
Built for the tools your team already uses
Why teams work with us
15+
countries served remotely
< 15 min
critical incident response
24/7
support from the USA and India
Practices
Four practices. The same people from first test to final evidence.
One named lead scopes the work, the practitioners who test it also fix and govern it, and our operations team keeps it running.
Simulated environment, real workflow.
AI Security practice
Your AI has an attack surface. We test every edge of it.
Models read untrusted text, agents call real tools, and retrieval pulls in documents nobody reviewed. We attack that chain end to end, and every finding ships with the governance control it evidences.
AI Red Teaming Prompt and indirect injection, tool and agent abuse, data exfiltration and guardrail retests across LLM apps, agents, RAG and MCP servers.OWASP LLM Top 10 · MITRE ATLASAI Governance An AI management system, risk register and impact assessments that one control set can evidence for every framework.ISO/IEC 42001 · NIST AI RMF · EU AI ActSecure AI Adoption Architecture review, AI usage policy, shadow-AI discovery and data controls for copilots before they reach production data.NIST AI 600-1 · your data policy
Test-to-Evidence
A finding isn't done when the report is.
Most firms hand over a PDF. We follow each finding through the fix, into detection, and into your audit file.
Simulated environment, real workflow.
Where a quarter's findings end up.
Every finding is tracked to one of two ends: a passed retest filed as evidence, or a risk your owner accepted in writing. Nothing stays in a PDF.
| Reported | 142 |
|---|---|
| Fixed | 133 |
| Retest passed | 131 |
| Evidence filed | 131 |
| 9 risks accepted in writing | signed by owner |
| 2 in fix window | inside SLA |
Example engagement
Measured in fixes, not in findings.
What a security lead sees each week once an engagement is running: what got fixed, how fast, and what is proven. The numbers below are sample data from an illustrative engagement, not client results.
- Findings fixed and retested
- 133
- of 142 reported
- Median time to remediate
- 6.5h
- down from 38h in month one
- Controls with live evidence
- 94%
- +22 pts vs the previous audit
- Risks accepted in writing
- 9
- each signed by its owner
Simulated environment, real workflow.
| Period | Value (h) |
|---|---|
| Jan | 38.0 |
| Feb | 31.0 |
| Mar | 27.0 |
| Apr | 19.0 |
| May | 15.0 |
| Jun | 11.0 |
| Jul | 9.0 |
| Aug | 6.5 |
Simulated environment, real workflow.
Criticals close inside the agreed target.
Testers pair with your engineers on each fix and retest it the same week, so time to remediate falls month over month until it sits under the target. Your targets are agreed in your statement of work (SOW).
Managed SOC · MDR · NOC
The fix stays fixed because someone is watching.
Our analysts monitor around the clock with detections built from what our testers found in your environment. They contain threats under playbooks you approve, and you see every action they take.
- Detections seeded from your own pentest and red-team findings
- Containment under pre-agreed playbooks
- Named lead analyst and monthly reviews
Simulated environment, real workflow.
Platform
Our own tooling, so findings never fall out of view.
The console our consultants and SOC analysts work in, and you can too. Use it with an engagement or on its own.
Resilient Privacy Research
Notes from the practice.
AI security ·
AI agents and MCP: securing the tools your models can call
Agents turn text into actions. The Model Context Protocol makes connecting tools easy, which moves the real attack surface to tool permissions, tokens and the servers you install.
Read: AI agents and MCP: securing the tools your models can callAI security ·
Securing LLM applications: a threat model that fits on one page
Prompt injection is not a bug you patch once. Treat the model as an untrusted component and design the controls around what it can reach.
Read: Securing LLM applications: a threat model that fits on one pageExposure management ·
CTEM in practice: from vulnerability lists to exposure decisions
More scanners produce more rows. Continuous Threat Exposure Management turns findings into a repeating cycle that ends in fixes.
Read: CTEM in practice: from vulnerability lists to exposure decisionsHow we work
From first call to audit-ready.
- 01
Scope
A short call and a written scope: assets, objectives, frameworks, team and timeline. No surprises later.
Output: Written scope
- 02
Test
Practitioners attack the systems and the AI in scope. Critical findings reach you the day we confirm them.
Output: Same-day critical alert
- 03
Fix
We pair with your engineers on the fix, then retest it inside the agreed retest window.
Output: Retest result
- 04
Prove
Findings become detections and evidence, mapped to the controls your auditors and customers ask about.
Output: Mapped evidence
Tell us what you need tested, governed or proven.
A 30-minute call with a practitioner. You leave with a scope, a team and a timeline, whether or not you hire us.