Your AI can answer every user from only the data that user may see, and keep sensitive fields hidden on the way out. Relevant Software builds both controls into your prompts, retrieval, and agents, with a log for every check.
Relevant Software builds the control layer that sits between your users, your models, and your data stores, then ships it into the environment you already run.
Classification starts with the records your AI systems reach, and data governance supplies that list: who owns each class of record and who may reach it.
Masking runs at the moment a prompt leaves your application and again when the model answers, inside a latency budget your team agrees first.
A query returns only the passages the asking user may read. Relevant adds that check to a pipeline you already run, and where there is no pipeline yet, a RAG development engagement builds one with permissions in from the first index.
Agents reach tools and databases under scoped credentials, and every call gets inspected before it carries data outward.
Some controls belong inside your own product, and Relevant Software builds them as software your company owns and extends.
Analytics, testing, and model training run on datasets that keep their statistical shape while identities stay protected.
Discovery shows which AI tools your teams use today and which data classes travel to each one.
Tuning turns a working ruleset into one your teams trust, measured against your live traffic before enforcement widens.
Relevant Software writes the detection rules that watch every AI data path and routes alerts into the queue your responders already work. Adversarial testing of the same models and agents runs through our AI security services.
You get one list of every model, agent, retrieval index, prompt store, and log sink you run, with the regulated data classes marked against each. Your security reviewers read it without interviewing your engineers, and you choose the order the controls arrive in.
Detectors get written for your field names, formats, and languages, then reviewed with the owner of each data domain. Sign-off arrives as a ruleset file in your repository.
Relevant’s engineers write each control into your codebase and open it as a pull request with a test that shows the rule catching what it should. You keep approval and ownership.
Shadow mode runs first, so precision and recall get measured on your own traffic before a single request is blocked. Results come back as a per-class report with thresholds you approve.
Alerts route into your SIEM and on-call rotation, and the tool-call traces they read come from instrumentation Relevant installs at handover. AI agent monitoring and orchestration extends those traces into dashboards, a kill switch, and rollback.
You close the rollout owning a working generative AI DLP: rules in your repository, enforcement in your live request paths, and monitoring in your own tooling. Proof of what the AI touched and what the controls did takes one download whenever someone asks.
246 projects delivered, including banking, healthcare, and energy builds where data handling carried the sign-off before the feature shipped.
98% client satisfaction, earned on programs where the control layer shipped alongside the product roadmap rather than behind it.
96% employee retention keeps the engineers who wrote your classifiers available when you extend them to new systems.
Our AI-assisted development practice gives you up to 50% faster delivery and up to 20% fewer bugs, with every release still passing senior code review.
ISO 27001, GDPR, and HIPAA-aligned delivery governs how our engineers handle your repositories, your data samples, and your production access from day one.
OWASP Top 10 for LLM Applications, NIST AI RMF, the EU AI Act, and MITRE ATLAS shape the threat model and the evidence pack, so your reviewers read labels they recognize.
Two numbers set the price, and both are countable on your side before any call.
The first is how many places your data changes hands: an application that assembles prompts, an index that answers queries, an agent that holds credentials, a store that keeps traces.
The second is how many data classes you have to defend, because each class needs its own detector, its own test set, and its own tuning round.
Those two numbers turn the first call into a scoping conversation, and a roadmap with effort and sequence follows within 14 days of kickoff. If counting them is the hard part, contact us and we do the inventory together.
A DLP tool enforces at the layers it can attach to: a browser extension, an endpoint agent, a network proxy, a SaaS API. Your own product runs below all of them. The prompt is assembled inside your process, the query hits your index, the agent calls your database with your credentials, and your framework writes the traces, so none of that traffic crosses a boundary the tool inspects.
Yes, we can. A running system has three seams where controls fit without touching business logic:
Middleware at those three points satisfies most requirements. What moves the effort is whether prompt assembly sits in one place or spreads across services. If it is spread across a dozen call sites, the first job is consolidating them.
Four paths escape most inventories, each for its own reason.
Control engineering puts policy into the live request path, for data moving through your AI right now. Data anonymization services produce safe copies of your datasets, so training, testing, and analytics run on material that carries the same shape and none of the identities. AI data protection software development builds the policy engine or the classifier as a product you own, for when your team extends it without us or ships it to your own customers.
Choosing is straightforward: live traffic points to control engineering, copies of production sitting in dev and analytics point to anonymization, and a need to own the mechanism points to the software build.
Relevant Software maps every path your data travels through your AI systems and builds the controls that travel with it.
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