Artificial intelligence is rapidly becoming a key part of everyday workflows. While this brings incredible gains in productivity, it also introduces new security risks. For agencies and organizations that handle sensitive information, these risks present an urgent challenge.

At Samtek, we believe proactive security posture is essential for adopting AI responsibly and effectively. Our proactive security posture has four key elements:

1. Establish strict compliance and data handling policies
2. Control access to internal knowledge bases
3. Protect prompts and tool use
4. Build robust observability and monitoring

Strict Compliance & Data Handling Policies

The foundation of proactive AI security is an AI use policy. Any organization using AI with sensitive information needs strict compliance and data handling policies.  

When your agency handles sensitive data like Personally Identifiable Information (PII) or Personal Health Information (PHI), it’s important to be aware of how third-party AI models use your data. By default, many public chatbots record and use conversations as training data. This could expose sensitive information to a third party, where it could become part of a data breach.

For federal workloads, you should take extra care to ensure that models are hosted and inferenced only from within FedRAMP-authorized regions, and logging is kept under agency control.

We help teams build and implement policies that include several layers of protection:

• Data Use Controls: Instructing employees to opt out of data harvesting features in public tools.
• Model Deployment: Using local or private models that don’t share data externally.
• Clear Guidelines: Defining when you should (and shouldn’t) use AI tools with sensitive information.
• Security Training: Accompanying the policy with security awareness training that informs workers of the rules to follow and the vulnerabilities to monitor.

Knowledge Base Access

Next, controlling access to internal knowledge bases is another key element to proactive AI security. You can control knowledge base access by taking these precautions:

1. Tag at ingest; enforce at query. When you index documents for retrieval, attach access metadata (department, clearance, project, owner). At query time, filter results using the caller’s identity and roles before the model sees any content.
2. Prefer attribute-based access-control (ABAC) over ad‑hoc rules. Use attribute‑based access control, so a single policy governs both the retrieval store and any downstream caches. Keep the authorization decision independent from the model.
3. Prevent cross‑tenant leakage. Partition embeddings, caches, and vector indexes by tenant or department when the risk justifies it. Don’t rely on the model to “not mention” restricted content.

Prompt & Tool Safety

A third element to proactive AI security is protecting prompts and tool use. Here’s how you can protect prompts and tool use: 

1. Sanitize inputs. Reject or reshape inputs that attempt prompt injection, request hidden instructions, or solicit restricted topics. Use a lightweight pre‑filter to route risky inputs to a safe fallback or a human.
2. Constrain outputs. Post‑filter model responses for policy violations, PII leakage, or unsafe instructions. Block, redact, or downgrade confidence when needed.
3. Lock down tools. If your LLM can call tools (SQL, ticketing, email, cloud APIs), expose only the operations required, validate arguments, and enforce idempotency and rate limits. Use allowlists over denylists. Require human approval for irreversible changes.

Observability & Monitoring

Finally, follow these tips to build robust observability and monitoring and ensure a proactive AI security stance: 

1. Log information. Capture requesting user or service, request time, model and version, tools invoked and parameters, retrieval sources, policy decisions (allow/block), and response metadata.
2. Detect anomalies. Alert on spikes in blocked prompts, high similarity to restricted content, unusual tool use, or output patterns that indicate drift or hallucinations. Track model latency and cost to catch abuse and performance regressions.
3. Review and respond. Schedule regular reviews of sampled conversations and blocked events. Define an incident playbook that covers data exposure, model misuse, and supply‑chain compromise. Include rollback steps and communication templates.

No one wants to miss the fast-moving transformation and productivity that AI brings, but the security risks are real. If you dedicate some time and energy to proactively establishing AI security, you can safely unlock the benefits of AI while effectively managing risk.