AI Security
Securing artificial intelligence — from data to deployment.
AI systems introduce security, privacy, and governance risks that traditional controls were not built to address. HOKTER helps organizations adopt AI with confidence — assessing models, pipelines, and processes for the risks that matter.
What we review
AI security is broader than model security. It spans the data used to train and fine-tune, the models themselves, the systems that host them, the applications that consume them, and the governance that surrounds the entire lifecycle.
AI security services
- AI security assessment — structured review of models, pipelines, infrastructure, and controls.
- Model risk review — evaluation of model integrity, robustness, and exposure to adversarial manipulation.
- Data pipeline security — review of training data provenance, handling, and privacy controls.
- AI application security — reviewing applications that integrate AI models — including prompt injection, data leakage, and abuse scenarios.
- AI governance advisory — policies, oversight structures, and review processes for responsible AI use.
- Third-party AI review — assessment of AI capabilities embedded in vendor products and services.
Our approach
AI security is a young discipline, and much of the industry conversation is either hype or hand-waving. HOKTER takes a practical stance: identify the real risks for your organization, prioritize what matters, and help you address it in a way that fits your environment.
- Understand the use case What is the AI system for? What decisions does it inform or make? What would go wrong if it failed — or was manipulated?
- Map the data flows Training data, inference data, and outputs. Where does data come from, where does it go, and who can see it?
- Assess the model and pipeline Model provenance, training integrity, deployment configuration, and monitoring.
- Review the application layer Prompt handling, output filtering, tool use, and integration with external systems.
- Evaluate governance Policies, review processes, and oversight — the human structures that surround the technical.
- Prioritize and advise Concrete recommendations, ordered by risk and effort, aligned with how your organization works.
Risk categories we address
- Model integrity — poisoning, backdoors, and unauthorized modification.
- Adversarial manipulation — inputs crafted to cause incorrect or harmful outputs.
- Prompt injection — attacks that hijack AI application behavior via user input.
- Data leakage — exposure of sensitive information through model outputs, logs, or caches.
- Privacy — handling of personal data in training, fine-tuning, and inference.
- Third-party dependency — risks introduced through external models, APIs, or platforms.
- Governance gaps — unclear ownership, review, or escalation for AI systems.
Who this is for
Our AI security work supports organizations at different stages. Some are just beginning to explore AI and want a grounding in the risks. Others have already deployed systems and need a structured review. Others are building AI products and need to embed security from the start. We adapt the engagement to where you are.
Confidentiality
AI engagements often involve proprietary models, sensitive data, and internal strategy. All engagement material is treated as confidential, with limited access and documented handling.
Adopting AI with confidence
Whether you are evaluating a use case, reviewing a deployed system, or building governance, we can help.
Contact Security Team