From the prompt to the tensor.
Security built for complex AI. I-THAM protects your models from theft and your agents from misuse by recognising intent and behaviour, beyond the rules, filters and guardrails that slow AI down.
Built in Amsterdam. Now in private Early Access Programme.
- Apps and promptsIn-line prevention for prompt injection, jailbreaks and misuse of agent identities.
- Agents and MCP toolsTool calls checked against what the agent was asked to do.
- Data and RAGGuarding against poisoning going in and sensitive data leaking out.
- ModelsInterpretability that reads model behaviour, not just outputs.
- Runtime and inferenceResponse that contains threats while AI keeps serving.
- SiliconDeep-Stream detection inside the model, on the GPU and DPU where it runs.
AI is a stack. Attackers use all of it.
Security for AI takes more than spotting known threats. It means understanding how AI systems behave, what they are trying to achieve and where they are exposed, layer by layer.
- Predict riskAssessment before models and agents reach production.
- Prevent compromiseIn-line prevention at runtime.
- Preserve trustContinuous detection, response and assurance.
AI moves faster than its security
AI capability compounds every quarter. Security built on yesterday's patterns stays flat, and the gap between the two is where risk lives. We predict risk, prevent compromise and preserve trust, so assurance keeps pace with capability.
The Pyramid of Pain, rebuilt for the AI era
The next frontier is understanding what is behind the signal: intent. That is where attackers feel the pain, and where I-THAM focuses.
- The classic Pyramid of PainHashes, IPs and domains at the base, tools and TTPs at the top.
- Prompt strings and hashesTrivial to change. An attacker's model produces endless variants.
- Keys, accounts and IPsEasy to rotate between sessions.
- Jailbreak templatesSimple to mutate, translate or encode.
- Tool calls and MCP tracesHarder to hide. Unusual chains and exfiltration paths stand out.
- Agent behaviourTough to change. Plans that chase a goal leave a pattern.
- IntentWhat an attacker wants cannot be rephrased. This is where I-THAM focuses.
- The Deep-Stream viewI-THAM reads from the model signal up to intent.
Indicators are free to change
An attacker's own AI can rewrite a prompt, a key or a jailbreak template in seconds. The bottom of the pyramid no longer hurts.
Detection stays, as telemetry
I-THAM keeps the base automated and running at high volume. It informs the verdict instead of being the verdict.
Intent is where it hurts
Reading from the model signal up, I-THAM focuses on behaviour and intent, the things an attacker cannot change.
When the attack keeps changing, pattern matching cannot keep up.
Model-native: inside the model, not in front of it
Guardrails sit in front of the model and read text. I-THAM works inside the model itself, recognises what each request and action is trying to do, and explains every verdict.
Inside the model
Deep-Stream inspection follows models and agents down to the GPU and DPU, not just the text at the edge.
Intent, not indicators
Prompts, keys and jailbreak templates are free to change. Intent is not, so new and reworded attacks are recognised without a new rule.
Verifiable
Every verdict is explained, so security teams can see why a decision was made and audit it.
Built for the teams that run AI
Whether you own the risk, the models or the response, I-THAM gives each team what it needs from one platform.
Prove your AI is under control
Quantify AI risk in business terms and validate security, governance and compliance before anything reaches production.
Protect the models you build
Guard your models against theft and tampering and keep agents on task, with protection that reads intent and behaviour, beyond what guardrails can see.
Respond while AI keeps serving
Alerts that explain the intent behind an attack, not just the pattern it matched, with orchestrated response and MCP integration for the AI SOC.
Security you can verify, built in Europe
I-THAM is built in Amsterdam and is a member of the NVIDIA Inception program.
European and sovereign
Built in Amsterdam. I-THAM can run entirely in your own environment, so your data and models stay under your control.
Deploy anywhere
SaaS, private cloud, on premises or fully air gapped, depending on what your AI and your regulators need.
Verifiable
Every verdict is explained and every response action is recorded, so teams can audit what happened and why.
A partner for the long run
We work alongside your team from the first deployment onwards.
Five ways I-THAM protects AI
Before deployment, at runtime and after every decision. Each area works on its own and gets stronger together.
Deep-Stream Detection
Proprietary inspection for novel attack surface across models, agents and AI internals.
Layers: Models, SiliconExplore02In-Line Prevention
Stop prompt injection, jailbreaks, data exfiltration and misuse of agent identities as they happen.
Layers: Apps and prompts, Agents and MCP tools, Data and RAGExplore03Pre-Emptive Assessment
Validate security, governance and compliance before production.
Layers: all six, before productionExplore04Risk-Driven Optimization
Focus remediation where it matters most to the business.
Layers: all sixExplore05AI-Native Response Orchestrator
Orchestrate real-time response while the business keeps running.
Layers: Runtime and inferenceExplore
Autonomous AI is here. But can you trust it to act on your behalf?
The old question was whether our AI is secure. The one that matters now is whether we can trust it to act: to send the email, move the money or change the code on our behalf.
AI security, answered
What is I-THAM?
I-THAM is an AI security company based in Amsterdam, the Netherlands. We build interpretable, verifiable defences that protect mission-critical AI systems across the full stack, from the prompt to the tensor.
What does "from the prompt to the tensor" mean?
AI risk is not only in the chat window. It spans apps and prompts, agents and their tools, data and retrieval, the models themselves, the runtime that serves them and the hardware they run on. I-THAM is designed to protect every one of those layers.
How is I-THAM different from other AI security tools?
Many AI security tools are guardrails. They sit in front of the model and check prompts and outputs against rules and lists of known bad inputs, which holds only until an attack is reworded. I-THAM is model-native. It works inside the model and recognises the intent and behaviour behind each request and action. That lets it catch new attacks, stop attempts to steal or tamper with models and keep agents on task, going beyond what guardrails can see. Every verdict is explained, so teams can trust and audit it.
Why does I-THAM detect threats on the GPU and DPU?
That is where AI actually runs. Watching at the GPU and DPU lets I-THAM see how models behave, not just what they say, so threats can be caught before they show up in prompts or outputs.
Does I-THAM protect AI agents?
Yes. Every agent acts under an identity I-THAM recognises, so unknown agents and agents reaching beyond their permissions are stopped. I-THAM also checks what agents do, including tool and MCP calls, against the goal they were given, so risky actions can be stopped or held for a person to approve.
How is I-THAM deployed?
I-THAM runs as SaaS, in your private cloud, on premises or fully air gapped. Every option, including air gapped, is operational in under 30 minutes. It integrates with leading security and collaboration tools.
Is I-THAM available today?
I-THAM is available through a private Early Access Programme. Contact us to find out how I-THAM can secure your AI stack.
Where is I-THAM based?
I-THAM is headquartered in Amsterdam, the Netherlands, and works with organisations that run AI in mission-critical settings.
See what your AI is really doing.
AI security is no longer only about detecting what happened. It is about understanding what an AI system is trying to achieve, and stopping harmful intent before it becomes impact.