Secure AI deployment, model and pipeline hardening, and adversarial red teaming for production AI systems.
AI systems break the assumptions that traditional security programs rely on. Trust boundaries dissolve when prompts execute, models leak training data, agents acquire and use credentials, and supply chains stretch through open-weights ecosystems and third-party APIs you do not directly control.
Versus works with engineering, risk, and governance teams to ship production AI that holds up under adversarial pressure. Our team blends offensive AI researchers, ML engineers, and governance specialists who have deployed AI in regulated environments — financial services, healthcare, and government.
We cover the full lifecycle: secure deployment architecture, model and pipeline hardening, adversarial red teaming, and governance programs aligned to NIST AI RMF, ISO/IEC 42001, and the EU AI Act.
Each engagement is led by senior operators. Scope is shaped to your environment, not pulled from a template.
Reference architecture review, identity and trust-boundary design, and secrets management for production AI and agent stacks.
Training-pipeline security, supply-chain controls for open-weights models, and runtime hardening for inference endpoints.
Prompt injection, jailbreak, data exfiltration, model theft, and tool-abuse testing against your specific deployments.
Threat modeling and testing for AI agents with tool use, file access, browser control, and downstream credentials.
Training-data integrity reviews, fine-tuning pipeline controls, and detection for poisoned weights and backdoored models.
Programs aligned to NIST AI RMF, ISO/IEC 42001, EU AI Act, and emerging financial and healthcare regulator expectations.
A consistent rhythm whether the engagement is a single audit or a multi-quarter program.
AI system inventory, data-flow mapping, and risk-tier classification across the organization.
Adversary assumptions, abuse cases, and impact analysis tailored to each AI system’s deployment context.
Adversarial red teaming, penetration testing of pipelines and endpoints, and governance gap assessment.
Policy, control, and assurance program. AI risk reported to the risk committee like any other material exposure.
If yours isn’t here, the hotline and engagement intake both reach a senior partner.
Yes — and arguably more. The security boundary moves to your prompt layer, your data going out, and the trust you place in third-party model behavior. "It is just an API call" is not a threat model.
No. Evaluation measures capability and bias on benchmarks. Red teaming attacks the deployed system — prompts, tools, retrieval, agents — under adversary assumptions. They are complementary.
Yes. We help organizations classify their AI systems, build the documentation and conformity assessment evidence required for high-risk systems, and operationalize ongoing compliance.
Agentic systems are the highest-risk class we work on. Tool use, credential delegation, and autonomous decision loops require threat models that look more like insider risk than traditional appsec.
AI engagements frequently sit alongside these capabilities. The same operating doctrine, the same partners.
Most engagements begin with a 30-minute scoping call. We’ll tell you within that call whether we’re the right fit.