AI security for enterprises that cannot afford to get it wrong.

Why AI security is different from model safety

Most AI security tools focus on the model: content filtering, prompt hardening, output validation. These matter. But they miss the actual attack surface.

AI agents are not models. They are autonomous programs that call tools, access databases, send emails and chain actions across systems. The damage comes from what the agent does, not what the model says. A prompt injection does not need to produce harmful text. It needs to make the agent take an unauthorised action.

Securing AI requires a fundamentally different approach: runtime interception at the agent layer, not just guardrails on the language model.

How TapPass secures AI agents at runtime

TapPass sits between your AI agents and the real world. Every agent interaction passes through a governance pipeline that evaluates it against your security policies before it executes.

Intercept

Every prompt, tool call and model response flows through the TapPass pipeline. Nothing reaches the outside world without evaluation. The integration is a single configuration change in your agent framework: OpenAI, Anthropic, LangChain, CrewAI, or any OpenAI-compatible endpoint.

Evaluate

58+ pipeline steps analyse every interaction in real time:

• Prompt injection detection, catches direct, indirect and multi-turn injection attempts before they reach the model
• PII scanning, language-specific recognisers for Dutch, German, French, Spanish, Italian and Belgian contexts
• Secret detection, prevents API keys, credentials and tokens from leaking into prompts or responses
• Data exfiltration prevention, detects attempts to extract sensitive data through tool calls or encoded outputs
• Tool governance, enforces least-privilege per agent, per tool, per operation
• Cost and budget controls, per-agent and per-organisation token and spend limits

Enforce

When a policy violation is detected, TapPass acts before the damage happens:

• Block, reject the request entirely, with a reason logged to the audit trail
• Redact, strip sensitive data from the request and let the agent continue
• Pause, hold the action for human approval before executing
• Alert, flag the event to your SIEM, Slack or webhook endpoint

Prove

Every decision generates a tamper-evident audit record, SHA-256 hash-chained, with full provenance: which agent, which tool, which data, which policy, what verdict and when. This is the compliance evidence that auditors and regulators require under the EU AI Act, GDPR, DORA and NIS2.

AI security by industry

Bad AI agent decisions carry different costs in different sectors. TapPass adapts to your industry's specific risk profile and regulatory requirements.

Real-time monitoring, not periodic audits

Traditional security monitoring was built for human-speed workflows. A human reviewer handles twelve cases per hour. An AI agent processes two hundred. When the human makes a bad call, it affects one case. When the agent gets a rule wrong, two hundred cases go out the door before the next audit cycle catches it.

TapPass provides real-time monitoring at the speed AI actually operates. Every decision is evaluated as it happens, not after the fact. The faster your agents get, the more critical this becomes.

The platform tracks per-call metrics: wall time, pipeline latency, LLM latency, tool execution time, token counts, cost and step-by-step timing breakdowns. Anomalies surface immediately, not in next quarter's compliance review.

How TapPass compares

Most tools in this space solve one piece of the AI security puzzle. TapPass is a full-stack governance platform.

Deep dives on AI security

We publish regularly on the specific challenges of securing AI agents in production. Start here: