In the AI space, moving fast without security discipline is an invitation to attack. We build security in at inception — and leave your organization capable of sustaining it.
The AI space is moving at a pace that creates enormous opportunity — and enormous attack surface. New frameworks, new model providers, new deployment patterns, and new supply-chain vectors appear every week.
Most organizations applying AI for the first time are not thinking about security until something goes wrong. We think about it before the first line of code is written.
Our security approach is not a compliance checkbox. It is built into our engineering workflow, our dependency selection criteria, our deployment patterns, and the monitoring tools we hand off to your team when the engagement ends.
Threat modeling, secure architecture review, and dependency vetting before development begins. Security requirements are first-class requirements.
Code review for injection, prompt injection, deserialization, and access control. Dependency scanning at every merge. No known critical CVEs shipped.
Secure configuration defaults, hardened headers, minimum-privilege service accounts, encrypted secrets management, and validated access controls.
We provide the monitoring tools, alerting configurations, and patching guidance needed to sustain security posture — long after the engagement is complete.
Not security theater. Specific, documented controls applied to every AI engagement.
LLM-generated content is treated as untrusted before any downstream use. We clearly delimit system instructions from user-supplied input and never allow user content to override system-level controls.
Every new dependency passes a structured selection gate: maintenance status, known CVEs, ownership history, install-time behavior, and version pinning. We reject packages with recent unexpected transfers or anomalous releases.
No hardcoded secrets, tokens, or credentials. Secrets managed through environment isolation and approved vaulting solutions. Audited access controls and rotation policies from day one.
Automated daily scanning of dependencies for new CVEs using pip-audit and npm audit equivalents. Alerts before vulnerabilities reach production. Diff-based tracking so new issues are immediately visible.
We prefer .safetensors format for model weights. Any use of legacy loading formats is explicitly reviewed, and weights_only=True is enforced — guarding against active RCE vectors in model deserialization.
All external input is validated with allowlists, strict schemas, length limits, and type checks. LLM endpoints exposed beyond local use are protected with rate limiting, per-session caps, and token usage monitoring.
Operating in Controlled Unclassified Information (CUI) environments requires a different level of discipline than standard commercial software development. We have first-hand experience designing and operating within NIST 800-171 compliant systems.
This means your AI deployment can be designed from the outset to meet or complement your existing compliance posture — not require a costly retrofit after the fact.
Data classification, access controls, and handling procedures aligned with CUI program requirements.
Role-based, least-privilege access with audit trails — aligned to 800-171 AC family requirements.
Comprehensive, tamper-evident logging of all sensitive AI actions — supporting accountability and incident response.
Firsthand experience containing and recovering from cyber attacks in operational defense-adjacent environments.
AI introduces security challenges that traditional secure coding practices don't fully address. We stay current on all of them.
Malicious instructions embedded in documents, user input, or external data that attempt to hijack model behavior. We design retrieval pipelines and agent systems with clear trust boundaries and input sanitization at every ingestion point.
Adversarial documents crafted to corrupt retrieval results or inject false context into LLM responses. All ingested content is treated as untrusted and subjected to content scanning, type validation, and size limits.
Loading model weights from untrusted sources using unsafe formats (`.pkl`, `.pt`) is an active remote code execution vector. We enforce safe loading practices and prefer validated safetensors formats from audited sources.
Attackers who can reach an LLM endpoint can generate thousands of expensive requests. We apply rate limiting, per-user token caps, input length validation, and usage monitoring to all externally exposed AI endpoints.
The AI/ML package ecosystem has seen active supply-chain attacks (backdoored PyPI releases, typosquatted packages). Every dependency is vetted against our selection gate before introduction, and version pins are enforced in production.
In multi-agent systems, a compromised agent can impersonate trusted orchestrators. We treat messages between agents as untrusted unless explicitly authenticated, and enforce output validation before downstream action execution.