Service · Security
Cybersecurity engineered in, not bolted on.
We secure the software and AI you ship — threat modeling, secure development, code and dependency review, supply-chain hardening, and AI/LLM security. Engineering that closes the vulnerabilities that actually reach production, inside your own cloud, with every artifact assigned to you.
Secured where it's built
The real problem
Most security problems trace back to how the software was built.
Security gets treated as a gate at the end instead of a property of the design. A feature ships, a pen test finds a flaw months later, and it costs an order of magnitude more to fix — NIST puts production remediation at roughly 30× the cost of fixing the same defect during development.
The cheapest vulnerability is the one your architecture never allowed. That's the work we do — and it's worth being precise about what it is, and isn't.
What this is
Secure software engineering + application and AI security — we design, build, review, and harden software so vulnerabilities never ship, and secure the AI/LLM layer being bolted onto products.
What this isn't
Not a managed SOC. No 24/7 monitoring, no incident-response retainers, no network monitoring. If that's what you need, we'll point you to a partner — and secure the code and AI layer they don't build.
More expensive to fix a vulnerability in production than during development — the gap secure design closes.
Where it changes the work
Where secure engineering changes the work — and what each delivers.
Security as engineering shows up in specific places in the build — for each, what it does, the benefit, and one illustration.
Threat modeling & secure design review
Trust boundaries, data flows, and abuse cases mapped before code locks in. Whole classes of vulnerability designed out.
A new partner API is threat-modeled; an auth flaw leaking tenant data becomes a whiteboard fix, not an incident.
Secure SDLC & security code review
Auth, input handling, secrets, and the OWASP Top 10 reviewed on every change — not once a year. Fewer exploitable defects per release.
An injection bug from a concatenated SQL query dies in the pull request, not in production.
Software supply-chain & dependency security
Every dependency inventoried in an SBOM, pinned, and screened for malicious packages. The open-source attack surface, controlled.
A typo-squatted npm package is blocked at install instead of shipping a credential stealer.
AI & LLM application security
Prompt-injection defense, scoped tool calls, output validation, and data-leak controls around models. AI shipped without a new breach class.
An "export the customer table" prompt is contained by permissioning and output checks — OWASP's #1 LLM risk.
Compliance-aligned architecture
Audit-trail, least-privilege, and access controls built toward what your regulators require. Compliance evidence falls out of the build.
A fintech feature ships with logging and least-privilege built in, so the SOC 2 audit reads existing artifacts.
Security hardening of existing apps
Auth, dependencies, config, and new AI surfaces in live software reviewed and fixed. Known risk reduced without a rewrite.
A legacy app's critical CVEs are patched in a controlled release, closing exposure without pausing the roadmap.
Fix it left, not in production. A vulnerability caught in design costs roughly 30× less than the same flaw found in production — so we move security to the pull request.
As of June 2026 · revisit quarterly
What building security in does to the numbers — the measured impact.
Independent, named-source findings on secure-engineering economics — cited as third-party evidence, never Silicon Prime's own client results.
Remediation cost. Fixing a vulnerability in production vs. during development.
Average breach cost, 2024. Up 10% YoY; extensive security AI/automation saved $2.2M per breach.
Rise in open-source malware. 778,500+ malicious packages catalogued since 2019 — a primary attack vector.
AI breaches by 2027. Share stemming from improper cross-border GenAI use as adoption outpaces governance.
What's included
What our cybersecurity services cover.
Concrete scope, mapped to where vulnerabilities are born and where AI is exposed.
Threat modeling & architecture review
We model trust boundaries, data flows, and abuse cases and deliver a prioritized risk register — flaws ranked by exploitability and impact, with fixes specified.
Secure SDLC & security code review
We wire manual and static review of auth, input validation, secrets, and the OWASP Top 10 into pull requests — not into a late gate.
Software supply-chain security
We generate and govern an SBOM, pin and screen dependencies, and add a pipeline policy gate so a poisoned package fails the build instead of shipping.
AI & LLM security
We harden AI features the way we build them: prompt-injection defense, scoped tool calls, output validation, retrieval-data isolation, and human-in-the-loop escalation where a wrong answer is costly.
Compliance-aligned engineering & remediation
We architect toward the controls your audits require — least-privilege, audit logging, encryption, data-residency — and fix what review finds, in code and AI surfaces alike, under tested releases.
What you get — all assigned to you under full work-for-hire IP
How it runs
How a secure-engineering engagement runs.
The same delivery model behind all our AI and software work, tuned for security — one accountable lead, fixed scope, no handoffs.
STEP 01
Assess
Scope the system, the data it holds, and the threats that matter — starting from an NDA and a security review.
Output: a scoped engagement & risks ranked by impact
STEP 02
Threat-model
Map trust boundaries, data flows, and abuse cases across the application and its AI surfaces.
Output: a threat model & a prioritized risk register
STEP 03
Harden
Fix the design and the code in your own cloud tenant — secure review on every change, dependencies screened and gated, AI integrations guarded.
Output: remediated, tested software behind your access controls
STEP 04
Verify
Re-test the fixes, confirm the controls hold, and train your team to keep the discipline running.
Output: a verified system & a team that owns the practice
Track record
The record here, stated honestly.
We'll be straight about evidence: most of our public, named outcomes are software-delivery and reliability engagements, not standalone "security audit" case studies. So here's the real record — each labeled for exactly what it demonstrates.
Silicon Prime is a Stanford-rooted Responsible AI lab, founded in 2011, run by founder Kelvin Tran — 20+ years of production engineering. If a request falls outside secure software and AI engineering — a managed SOC, an IR retainer — we'll say so rather than sell it.
Transaction-system integrity at scale
For YardClub we built the full marketplace, including payments and transaction infrastructure that processed $120M+ before Caterpillar acquired the company in 2017.
Payments are unforgiving on input handling, authorization, and data integrity — the same muscles this service applies.
Pre-release quality discipline
For BJ's Restaurants (200+ locations), AI-assisted code review, regression prevention, and monitoring held twice-a-week releases with zero critical defects across four years.
Shifting defect-catching left into review and pre-release gates is the operational core of secure SDLC.
Long-lived, maintained code
For Bridge Athletic — live since 2012, used by USC, the LA Rams, and MLB/MLS teams — we carried one codebase through 12+ years of modernization and dependency re-engineering without downtime.
Keeping dependencies current over a decade is the supply-chain hygiene this service formalizes.
Why secure it with us.
Security is an engineering property here, not a service line we resell. We build the software and the AI, so we secure them where vulnerabilities are born — the design and the code — not just scan the outside after the fact.
We secure the AI layer most providers can't. Prompt injection, tool-call abuse, and model data-leakage are a 2026 attack class; we engineer against the OWASP LLM Top 10 because we ship these systems too.
Honest scope, no fear-selling. We do secure engineering and application/AI security — and we say where that ends, rather than overclaim 24/7 monitoring we don't run.
Founder-led and built to transfer. The person who scopes it answers for it; threat models, fixes, and the screened pipeline are assigned to you, and your team is trained to keep the practice running.
Where it matters first
Where secure engineering matters most first.
Healthcare
PHI-handling systems hardened inside HIPAA-aligned architectures, with access logging and data isolation engineered in, not retrofitted.
Healthcare software →Fintech
Payments, real-time decisioning, and fraud-sensitive systems where authorization, audit trails, and input integrity are the product.
Fintech software →AI-enabled products
Teams adding LLM and conversational features who need the new AI attack surface secured before it ships, not after a breach.
AI development →Questions buyers ask before they hire.
Thirty minutes · no scare tactics
Ready to make security a property of your software, not a fire drill?
Bring the system — the application, the AI features, the dependency tree you're unsure about — and we'll tell you honestly where the real risk is, what it takes to engineer it out, and where our scope ends.