BLACK BAGSECURITY
Services
Black Bag Security combines offensive testing, adversary validation, AI security, and secure systems engineering under one operating standard: security claims should survive contact with evidence. Engagements are principal-led, bounded by explicit rules of engagement, and designed to leave you with decisions you can defend and fixes you can verify.
The work changes with the system and the decision you need to make. The standard does not: understand the trust boundary, exercise realistic failure paths, preserve evidence, explain the root cause, and verify whether remediation actually closes the issue.
Test the places where identity, trust, state, and data cross boundaries.
Start with an objective and test whether the defensive assumptions around it hold.
Treat AI as a system of models, tools, data, users, browsers, and control boundaries.
Design and harden systems so security is part of the architecture, not a compensating control.
The process is intentionally simple. Every phase exists to reduce ambiguity, control risk, and improve the quality of the final technical judgment.
Establish what needs to be known, what success looks like, and who owns the risk decision.
Agree scope, access, rules of engagement, safety controls, escalation, and data handling.
Use manual analysis and purpose-fit tooling to test realistic paths, not merely enumerate weaknesses.
Preserve enough evidence to reproduce material findings and support impact, root cause, and priority.
Translate findings into remediation decisions, then retest to distinguish mitigation from resolution.
Some engagements require analysis below the normal application or infrastructure layer. These capabilities are brought into the work when they materially improve the answer, rather than sold as disconnected activities.
Deep technical investigation where the failure mode, exploitability, or root cause is not obvious from surface testing.
Binary and protocol analysis to understand behavior, validate attack paths, and challenge defensive assumptions.
Targeted source, configuration, and dependency analysis when runtime behavior alone does not explain the risk.
Human-governed automation and evidence workflows for security research and testing where repeatability matters.
Defined assets, access, test objectives, and deliverables. Appropriate when the environment and decision boundary are already clear.
Begin with an outcome or defensive assumption and allow the testing path to follow the evidence within agreed rules of engagement.
Focused technical work for complex systems, root-cause analysis, hardening, architecture, or research questions that do not fit a conventional test.
You do not need to arrive with a perfect statement of work. Start with the system, the concern, the decision you need to make, and the constraints we need to respect.