AI Security Audits and LLM Penetration Testing in San Francisco

Why does San Francisco's AI density change the security math?

San Francisco is where the AI industry signs its leases. AI companies took 31.3% of new office leasing in the city in the first half of 2026, about 2.9 million square feet, according to Savills and CompStak. In Mission Bay, OpenAI subleased Uber's buildings at 1455 and 1515 Third Street, nearly 500,000 square feet and the largest office lease in the city since 2018. In SoMa, Anthropic is headquartered on Howard Street, and the owners of 300 Howard market the street as AI Alley.

The money follows the same map. Of the 500 most capitalized AI firms on the planet, 70 sit in San Francisco, more than in any other city, per PitchBook data. Crunchbase counted $122 billion raised for AI in the Bay Area in 2025 through mid December, more than three quarters of all US AI funding. So a 200 person SaaS company in the Financial District is probably building on the same models, vector stores, and agent frameworks as the startup across the street.

That shared stack cuts both ways. Good patterns spread quickly here, and so do attack techniques. An indirect prompt injection that works against one support copilot often works against the next one built the same way. Your enterprise customers know this, which is why AI questions now sit in their security reviews next to the SOC 2 report and the annual pentest.

What does an AI security audit in San Francisco cover?

A useful AI security audit San Francisco buyers can show their own customers starts with one customer facing LLM feature plus everything it can reach, including its prompts, its retrieval layer, the tools it calls, and the data behind them. Larger scopes add internal AI tools, multi step AI workflows, and the AI vendors you depend on.

The testing is hands on. Our engineers attack the feature the way an outsider would, covering prompt injection through the chat box and through content the model reads, jailbreaks, data leakage across users and tenants, agent and tool abuse, RAG poisoning, and access control bypass. Each finding is mapped to the OWASP Top 10 for LLM Applications and to NIST AI RMF, so your security and compliance leads can file it where they already track risk.

• Prompt injection (LLM01), both direct from users and indirect from emails, tickets, PDFs, and web pages the model reads.
• Sensitive information disclosure (LLM02) and system prompt leakage (LLM07), including one tenant's data showing up in another tenant's answers.
• Excessive agency (LLM06), where an agent can send, delete, or buy something no user asked for.
• Data and model poisoning (LLM04) and vector and embedding weaknesses (LLM08) inside your RAG pipeline.
• Improper output handling (LLM05) and unbounded consumption (LLM10), the classic web bugs and cost abuse that sit around the model.

How is LLM penetration testing different from a standard pentest?

A standard penetration test checks authentication, sessions, database injection, and cloud misconfiguration. You still need one. LLM penetration testing adds an attack surface those tests were never designed for, which is plain language. Your model treats every sentence it reads as a possible instruction, and it reads a lot of text you do not control.

A quick note on terms. The phrase AI penetration testing usually means pentest tools that use AI to scan ordinary apps, which is a different product. What we sell is LLM security testing, where our engineers attack the AI features themselves, record every exploit that works, and check whether your guardrails hold against a patient attacker.

AI red teaming goes one step further. We chain small weaknesses, such as a leaked system prompt plus a tool with more permission than it needs, into a realistic incident like a refund issued, a record deleted, or a customer list sent to an outside address.

What will enterprise buyers ask about your AI in a security review?

If you sell to banks, health systems, or large enterprises, expect the security questionnaire to ask about your AI. Typical questions cover where prompts and outputs are stored, whether customer data trains any model, how you keep one tenant's data away from another, what an agent may do without a human, and when someone outside the team that built the feature last tested it.

That last question carries more weight than any policy document. A report with severity ratings, reproductions, and a retest showing the fixes held gives your security lead something concrete to attach to the deal.

We wrote this page for the CISO, VP Security, or Head of AI at a 50 to 500 person SaaS company with LLM features already in production. Few companies that size have an AI security specialist on staff, and hiring one in San Francisco is slow. BLS puts the median software developer in the metro at $186,640, and Levels.fyi reports median total pay of $364,000 for senior engineers across the Bay Area.

How long does the test take, and what lands on your desk?

It starts with a scoping call. We agree on the systems in scope, the threat model, and what success looks like, then you get a fixed price and a schedule before any testing begins. A focused review of one feature sits at the short end. A deep audit that spans several systems runs over several weeks.

Our engineers can implement the fixes alongside your team or hand the roadmap to your developers. Either way, the retest and a final sign-off report are part of the fixed price.

• An executive summary written for leadership and for your customers' security teams.
• Findings with severity ratings, so engineering knows what to fix first.
• A reproduction for every finding, with the exact inputs that triggered it.
• A prioritized remediation roadmap that maps each fix to the finding it closes.

Which California rules touch an AI product shipped from San Francisco?

The CCPA regulations from the California Privacy Protection Agency took effect January 1, 2026. Since then, certain processing needs a risk assessment before it starts, including automated decisionmaking technology used for significant decisions about lending, housing, education, employment, or healthcare. The ADMT duties themselves apply from January 1, 2027, and larger businesses and data sellers owe a cybersecurity audit, with the first reports due April 1, 2028.

Two narrower laws matter for some products. SB 243 covers companion chatbots and excludes bots used only for customer service, productivity, or technical help. SB 942, in force since August 2, 2026, requires providers of public generative AI systems to offer a free detection tool and to embed hidden provenance labels in AI generated image, video, and audio.

An LLM penetration test does not satisfy any of these rules on its own, and it is not the independent cybersecurity audit the CCPA rules describe. What it does produce is the evidence those processes ask for. None of this is legal advice, so take scope questions to your counsel.