Research & Intelligence — TTAN.IO
From investigation to operational control.
Independent AI security research translated into decisions, policy enforcement and evidence that operate before the model is reached.
The market context
AI adoption is moving faster than many security programs can adapt.
Generative AI, LLMs and autonomous agents introduce security behavior that traditional application controls do not fully model: natural-language instruction manipulation, delegated authority, tool execution, retrieval context and model-mediated decisions.
The gap is operational rather than theoretical. Security teams need to understand intent, constrain authority, protect sensitive context and preserve evidence around AI-driven actions.
- Prompt and instruction manipulation can change model behavior and downstream actions.
- Sensitive data exposure can occur through prompts, retrieval context, outputs and tools.
- Agentic authority increases the impact of a model or orchestration failure.
- Audit and governance expectations require more than ordinary application logs.
- Operational visibility must cover what was requested, what was allowed and what actually executed.
TTAN.IO research focuses on turning those failure modes into controls that can be tested, enforced and reviewed.
Where we are
An independent security, decision and evidence layer.
TTAN.IO sits between the application and the AI model. Security decisions are made outside the model's own reasoning path, so policy and authority do not depend on whether the model behaves as expected.
The model can propose an answer or action. TTAN.IO evaluates the request context, applicable policy and execution boundaries before the protected action is allowed to continue.
How we actuate
The model may suggest. The architecture decides.
| Layer | Operational function |
|---|---|
| Intent Classification | Classifies the request and its security intent before the protected model path is reached. |
| Policy Enforcement | Applies organizational rules such as access boundaries, data handling restrictions, rate controls and execution policy. |
| Decision Layer | Produces an independent allow, deny or review decision based on policy and risk context. |
| Evidence Generation | Creates independent decision and execution records; cryptographic signing can be applied to evidence where configured. |
| Fail-Closed Execution | If a required policy or evidence path is unavailable, the protected execution is blocked rather than silently passed through. |
Intent classification
We classify before we process.
Intent classification is used to understand what a request is trying to achieve before it enters a protected model workflow.
- Semantic intent — What is the user or agent actually asking for?
- Malicious intent — Does the request attempt prompt injection, jailbreak, data extraction or control bypass?
- Policy intent — Is the requested action compatible with organizational policy?
- Risk intent — What is the expected impact if the request is allowed?
Attack prevention
Controls are applied before protected execution.
| Attack vector | TTAN.IO control approach |
|---|---|
| Prompt Injection | Classifies instruction-manipulation signals and applies policy before the request is forwarded to a protected model path. |
| Data Exfiltration | Applies data-handling and context controls to prevent unauthorized sensitive information from entering or leaving protected flows. |
| Instruction Override | Keeps security policy and authorization outside the model's own instruction hierarchy. |
| Tool Abuse | Constrains tool permissions and execution authority before an agentic action is allowed. |
| Extraction and Abuse Patterns | Uses policy, anomaly signals and rate controls to identify repeated or high-risk interaction patterns. |
The research engine
Research feeds the control layer.
TTAN.IO research is valuable when a finding can be reproduced, measured and translated into an operational security decision.
| Research area | Focus | Operational output |
|---|---|---|
| Prompt Injection | Instruction manipulation and guardrail bypass techniques | Pre-model classification, policy and containment controls |
| Agentic Risk | Authority, tool-use and autonomous execution failure modes | Capability boundaries, authorization and fail-closed execution |
| Evidence & Assurance | Decision provenance, verification and replay | Independent evidence records for investigation and audit |
| Adversarial AI | Evasion, extraction, poisoning and related adversarial behavior | Threat signals, testing scenarios and response logic |
| Governance Alignment | Mapping operational controls to recognized security and risk requirements | Control mappings and reviewable evidence packages |
Framework alignment
Operational security first. Framework mapping follows the control.
TTAN.IO relates operational controls and evidence to the frameworks security teams already use, without treating framework alignment as a substitute for technical enforcement.
The TTAN.IO difference
Security authority remains independent from the model.
| Aspect | Conventional pattern | TTAN.IO approach |
|---|---|---|
| Security Location | Often embedded in the application or model workflow | Independent layer between application and protected AI path |
| Decision Timing | May rely on model output or downstream inspection | Policy decision occurs before protected model execution |
| Intent Classification | Often treated as content filtering | Security context used as an input to policy and authorization |
| Evidence | Application and provider logs | Independent decision and execution evidence |
| Authority | Can be coupled to runtime behavior | Security authority is separated from model instructions and provider behavior |
| Resilience | Failure handling varies by component | Required security dependencies can be configured to fail closed |
Our philosophy
The model may suggest. The architecture decides. The evidence survives.
- The model may suggest — model output is treated as untrusted until policy permits its use.
- The architecture decides — authority and policy remain external to the model.
- The evidence survives — decisions and execution outcomes remain available for review, investigation and audit.
Summary
| What We Do | Independent security, decision and evidence layer for AI applications |
| How We Do It | Intent classification, policy enforcement, separated authority, governed execution and independent evidence |
| Where We Act | Before the protected model path is reached |
| What We Address | Prompt injection, data exposure, instruction override, tool abuse, agentic authority and adversarial interaction patterns |
| What We Align To | OWASP LLM Top 10, OWASP Agentic AI, MITRE ATLAS, NIST AI RMF, ISO/IEC 42001 and EU AI Act requirements |
| Our Mission | Protect intelligence before the model is reached. |
Closing statement
Protect intelligence before the model is reached.
TTAN.IO turns research into enforceable security decisions and reviewable evidence for AI systems.
The model may suggest. The architecture decides. The evidence survives.