deeplayer Security

See what others miss.

AI introduces attack surfaces that traditional security frameworks were never designed to handle. deeplayer's security practice identifies, assesses, and remediates AI-specific vulnerabilities — before they become incidents.

AI security is not cybersecurity.

Traditional cybersecurity focuses on networks, endpoints, and data at rest. AI security requires an entirely different lens: model integrity, training data poisoning, prompt injection, output manipulation, supply chain risks in ML pipelines, and shadow AI proliferation.

What We Deliver

Purpose-built for AI-specific risk.

AI Security Audit

Comprehensive assessment of AI security posture. Covers model security, data pipeline integrity, access controls, and inference endpoints.

4–6 weeksOne-off or recurring

Vulnerability Assessment

Red-team exercises simulating adversarial attacks. Prompt injection, data exfiltration, adversarial inputs, and supply chain compromise.

2–4 weeks

AI Governance & Policy

Enterprise-wide AI governance frameworks. Acceptable use policies, model lifecycle management, and incident response procedures.

6–10 weeks

Compliance Mapping

Gap analysis against EU AI Act, ISO/IEC 42001, NIST AI RMF, NIS2, and DORA. Compliance roadmap with prioritized actions.

3–5 weeks

Why deeplayer

Different by design.

AI-native expertise

Not cybersecurity checklists applied to AI. We understand model architecture, training pipelines, and inference patterns at a technical level.

Adversarial thinking

Red-team exercises, not checkbox audits. We think like attackers targeting your AI systems, not compliance officers ticking boxes.

Governance meets enforcement

Policies that are actually enforced. We design governance frameworks with implementation in mind — not documents that live in a drawer.

Regulatory foresight

Frameworks designed for compliance longevity. We map to current and upcoming regulations so your posture holds as the landscape evolves.