Data leaks into the model
Personal data, business secrets, configuration files and keys. Everything an agent reads can reach an external model provider, and privacy regulators are not forgiving.
A secure SDLC for AI agents
Run AI agents where compliance used to say no.
ORION puts agents inside an architecture that polices its own boundaries. All traffic through one gateway, sensitive data masked before it leaves the organisation, permissions enforced by code. Your team works at full speed and security gets evidence.
Everyone wants AI. One question stops it: what about security?
Teams can see what working with agents delivers. Rollout stalls because nobody wants to sign off on the risk.
The agent can do too much
An agent running with human permissions will delete, overwrite or publish something nobody authorised. Instructions in a prompt can be talked around, so they are not a control.
No trail for the auditor
Who asked, about what, at what cost, and what was held back? Without measurement, governance is a promise, and audits do not accept promises.
You want AI, but not at the price of losing control?
You do not need your own security department or a large IT team to adopt agents responsibly. These are the situations where ORION pays off most.
"Our IT is a handful of people"
The same team runs servers, support and projects, and AI adds another front. ORION arrives as a finished foundation: you stand it up with a command, not a six month project.
"Our IT is outsourced"
An external provider runs the infrastructure and there is nobody to design a safe rollout with. Here the rules live in code and behave identically on both sides, whoever is on duty.
"We have no security department"
Nobody is there to say "this is fine" and own it. ORION brings ready policies: masking of sensitive data, narrowed permissions and hard gates that hold on their own.
"We do not know where to start"
There is no shortage of AI ideas, but none of them survives a risk assessment. You start with one process and one role, without a revolution across the company.
"Compliance says no"
Risk and legal ask about data protection, business secrets and the audit trail, and there is no answer. Now they get a report with numbers: what was held back, by whom and at what cost.
"We tried and shelved it"
Someone connected an agent to company data, a red light came on and the topic went back in the drawer. This time the architecture sets the boundaries, so there is nothing left to postpone.
Security built into the architecture
Six pillars that turn "we are afraid of AI" into "we know exactly what AI can do, and we can prove it".
One place to harden
Every role inherits from a single foundation. A change to security policy applies to everyone immediately, with no per-team exceptions and no way around it.
Guardrails mask data at the gateway
Personal data, business secrets and configuration values disappear before a request leaves the company. The guardrails run outside the agent process, so the agent cannot switch them off.
Boundaries enforced by code
A prompt can be talked around. A system that physically has no write operation cannot be. Critical transitions require a human approval, policed by gates in the code.
Agents hold no secrets
Only the service layer holds credentials. Tokens never enter an agent environment, so even a compromised environment carries no keys to your company.
All traffic through one gateway
Every model call and every tool call passes through a gateway with a key per role and a narrowed permission set. There are no side doors and none can be added.
Evidence instead of trust
How much sensitive data was held back, by type, without storing the values themselves. Cost and volume per role. A trail you put in front of an auditor instead of explaining promises.
How it works
The agent connects to nothing directly. All communication runs through the gateway, which cleans the data and lets through only what your policy allows.
The agent knows neither the addresses of company systems nor the model provider endpoint. It knows the gateway, and the permission lists sit out of its reach.
What it actually looks like
On the left, what the agent read inside company systems. On the right, exactly what left the building. The values are invented but carry valid checksums, because the gateway verifies the checksum rather than the shape of the text.
inputthe agent reads
Case 4821, individual client.
National ID 44051401359, ID card ABC123456.
Company: tax no 1234563218, reg no 123456785.
Transfer: IBAN PL61109010140000071219812874,
card 4111111111111111, CVV 123.
Contact: contact@piotrwlodarczyk.com, tel. 501234567.
Address: ul. Kwiatowa 12, 00-950.outputthe model provider sees
Case 4821, individual client.
National ID [ORION-GUARD-NATIONAL-ID], ID card [ORION-GUARD-ID-CARD].
Company: [ORION-GUARD-TAX-NO], [ORION-GUARD-REG-NO].
Transfer: IBAN [ORION-GUARD-IBAN],
card [ORION-GUARD-CARD], [ORION-GUARD-CVV].
Contact: [ORION-GUARD-EMAIL], tel. [ORION-GUARD-PHONE].
Address: [ORION-GUARD-ADDRESS], [ORION-GUARD-POSTCODE].configurationthe agent reads
[database]
host = db.internal.local
password = SuperSecretPass123
api_key = sk-proj-7f3a9c2e1b8d
port = 5432configurationthe model provider sees
[database]
host = [ORION-GUARD-CONFIG]
password = [ORION-GUARD-CONFIG]
api_key = [ORION-GUARD-CONFIG]
port = [ORION-GUARD-CONFIG]Inside configuration blocks every value is masked, not only the ones that look like a password. The database host and the port number are facts about your infrastructure too.
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An agent cannot grant itself access
The list of permitted systems comes from configuration the agent process cannot overwrite. Registering its own connection during a session is refused.
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An agent cannot weaken its own permissions
Deny rules beat any local change, and a separate guard inspects the whole command, including anything someone tries to hide inside another instruction.
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Masking goes all the way down
Guardrails walk recursively through the request body, tool results and the system prompt. Identifiers and account numbers are validated by checksum, not by pattern alone.
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Policies restore themselves
Protection is re-applied on a cycle, so an outage does not leave traffic exposed. There is no state in which the system quietly stops watching.
A whole delivery cycle, not a single assistant
Six roles cover the path from a requirement to a release. Each holds its own key and its own scope, so one role's mistake does not spread to the rest.
Step 1
Requirements analysis
A requirements sheet becomes an ordered breakdown into sprints, with a complete requirements package for each one.
Step 2
Backlog
Approved requirements turn into epics, stories, dependencies and a sprint. Without a human approval, nothing is created.
Step 3
UX design
Mockups come from approved requirements and wire into the team's workflow on both sides: in the issues and in the documentation.
Step 4
Development
A task enters an isolated workspace, code is written test first, and the result is a pull request ready for review.
Step 5
Testing
Acceptance criteria become scenarios and tests in the project's own stack, and the trail stays in the issue tracker, not only in the repository.
Step 6
Codebase knowledge
The repository map, critical paths and deployment traps are kept current, so knowledge does not leave when people do.
DORA, the AI Act and GDPR: what the auditor gets
Three regimes that in practice block agent rollouts. ORION answers them with architecture and with evidence.
DORA
Regulation 2022/2554Requires ICT risk management, third party oversight and event logging. A single exit point makes the relationship with the model provider one countable dependency instead of a scatter across workstations. A key per role attributes cost and traffic to a specific area, and the request record is produced by the infrastructure, so it does not depend on what the system says about itself.
AI Act
Regulation 2024/1689Emphasises human oversight, transparency and documentation. An approval gate checked in code is the technical form of oversight: without a human decision, the step does not execute. Roles with disjoint permissions make the limits of autonomy explicit in configuration you attach to the system documentation.
GDPR
Regulation 2016/679Data minimisation and control over transfers outside the organisation. Guardrails mask personal data before a request leaves, and the agent cannot disable them because they do not run in its process. A metric shows how many values of each type were held back, which turns a claim into a number.
Governance without measurement is a promise
Every guardrail and every unit of spend leaves a trail you can show an auditor.
0secrets in agent environments
100%of model traffic through the gateway
13guardrails for sensitive data
1change sets policy for everyone
Questions that come up most often
What is agentic coding?
Agentic coding is work in which an agent built on a large language model carries out multi step engineering tasks on its own: it reads the repository, plans a change, writes and runs tests, creates a branch and opens a pull request. What separates it from code completion is that the agent runs in a loop and uses tools, so it holds real access to company systems.
That access, rather than the quality of the generated code, is what security review is actually about. This is why ORION governs where the agent can walk rather than what it writes.
Can agentic coding be reconciled with DORA?
Yes, provided the agent's access is narrow, logged and reversible. An agent using a single exit point, with its own key, a limited permission set and a full request trail, fits the requirements on ICT risk management and third party oversight.
An agent running with its operator's permissions and talking directly to an external model provider does not. The difference sits in the architecture, not in an internal policy document.
What does the EU AI Act change for AI agents inside a company?
A deployer of an AI system has to ensure human oversight, transparency and documentation. You must be able to show who approved the system acting, what data reached it and where autonomy ends.
An architecture in which critical transitions require an approval checked in code produces that evidence. A line in the agent's instructions does not, because instructions can be worked around through the conversation.
How do you stop personal data leaking into a language model?
Masking has to run outside the agent, in a layer the agent cannot switch off. In ORION the gateway does this: a request passes through guardrails that walk recursively through the message body, tool results and the system prompt, replacing detected values with a marker.
Identifiers and account numbers are validated with checksums, so masking does not rest on text pattern matching alone and misfires less often in both directions.
Does ORION work with any AI assistant?
The control layer sits between the agents and the rest of the world, so it is not tied to a single model provider or to one tool your team happens to use. Swapping a provider is a gateway configuration change, not a rebuild of the environment.
That matters at purchase time: you do not buy vendor lock-in together with your security. The policies stay yours, whatever wins the market in two years.
Does ORION run on your own infrastructure?
Yes. You run everything yourself, locally or in a private cloud. Only the service layer holds credentials and agent environments hold none.
The sole outbound path is the gateway reaching the model provider, which keeps a deployment inside data residency and provider control requirements.
How long does it take to stand up?
The environment comes up with a command, not a project. The foundation, the gateway and the guardrails start together and the roles join as clients, so a first task can travel the whole path on day one.
Agreeing policies with your security team usually takes longer than the technical rollout. You arrive at that conversation with metrics and a written description of the boundaries.
Let us talk about a rollout at your organisation
We will show a working environment live: roles at work, guardrails holding back sensitive data, and the metrics your auditor will see. No commitment.