An end-to-end framework for governing, securing, and assuring AI systems. AI rewrites the attack surface and forces a rethink of how you secure data, models, and outcomes, so security has to meet governance, risk, and compliance in one motion.
AI is the inflection point. It introduces new trust models, new attack vectors, and new ways for sensitive data and proprietary models to leak. Treating an AI system like classic infrastructure misses most of what is actually at risk. The framework secures AI as a system: data, models, pipelines, prompts, and outputs are first-class assets, protected on their own terms.
Security alone is not enough. Governance, risk management, and compliance belong in every stage of the AI lifecycle, not bolted on at the end. That means addressing AI-specific threats directly: prompt injection, data poisoning, model theft, adversarial inputs, agent impersonation, and the older risks that AI quietly amplifies.
The goal is resilience, not just prevention. Workflows need explainability, accountability, and the ability to roll back when something goes wrong. And all of it has to hold up against the regulatory regimes now arriving: the EU AI Act, the NIST AI Risk Management Framework, and the relevant ISO standards.
Why AI risk differs from classic cyber, and how to map AI assets: data, models, pipelines, and agents.
Prompt injection, data poisoning, model theft, adversarial inputs, and agent impersonation, plus the older threats AI amplifies.
Scoring across governance, risk, compliance, data protection, model ops, and resilience, returned as a maturity heatmap and gap report.
Risks tied to real deployments: chatbots, copilots, analytics, code generation, viewed through a business lens of fraud, IP, and reputation.
When to build AI security and GRC expertise in house, when to buy tooling, and when to borrow it from a partner.
Model pipelines, API security, observability, and agent identity, grounded in data security, identity, and resilience.
An AI governance charter, model risk templates, and compliance overlays for the EU AI Act, NIST AI RMF, and ISO.
Threat modeling for AI systems, a risk scoring method, and remediation planning you can actually run.
Framing for AI security investment in the terms boards weigh: avoided fines, avoided downtime, and preserved trust.
Response paths for prompt injection, data leakage, model poisoning, and agent compromise, with rollback to a minimum viable recovery.
Map your AI assets, score readiness across governance, risk, compliance, data, and resilience, and catalog the risks in your real use cases before spending a dollar.
Stand up a security reference architecture, a governance charter, and a risk management playbook, with the build, buy, or borrow call made for each capability.
Continuous monitoring of models, prompts, and agents, compliance attestation and audit trails, and tested incident response with rollback when it counts.
Adapted for the sectors where the compliance bar is highest: financial services, healthcare, government, and manufacturing.
The usual way in is a readiness assessment: an honest score of where you stand on each dimension, a maturity heatmap, and the gaps worth closing first. From there we move into advisory, building the architecture, governance, and operating discipline to close those gaps in the order your organization can actually support.
The work is independent. Sponsored, never controlled, and pointed at what protects the business rather than what a vendor wants to sell. See the full portfolio, read the services, or write to howard@phronia.co.