Cyberbase AI: Bridging the Gap Between Security and Revenue

Enterprise deals are stalling because security and legal teams are trapped in administrative silos. Cyberbase fixes this by automating contract redlining and security questionnaires using a dynamic "Context Engine." The result? Deals that used to take weeks to close are now finalized in hours.

Enterprise software is changing. Right now, there is a massive bottleneck sitting right where cybersecurity compliance meets revenue generation. Every year, global businesses waste billions of dollars because their compliance and legal workflows are completely disconnected.

While companies pour money into standalone contract management and risk software, sales cycles still hit a brick wall. Why? Because corporate knowledge is fragmented. When legal, security, and sales teams use different tools, deals regularly stall for four to six weeks just to manually reconcile security documents. In fact, data shows that a significant chunk of massive enterprise deals are ultimately canceled entirely due to this friction.

Historically, companies have treated security as a defensive cost center. Cyberbase flips that script. By unifying contract redlining, Due Diligence Questionnaires (DDQs), and external trust centers into one workspace, Cyberbase turns security into a strategic revenue driver.

The Origin Story: Built by Operators Who Felt the Pain

Founders Jon McLachlan and Sasha Sinkevich built Cyberbase AI after witnessing firsthand how siloed compliance documents constantly killed enterprise deals.

Cyberbase wasn't built in a vacuum. It was created by Jon McLachlan and Sasha Sinkevich—two Chief Security Officers who previously built security programs for giants like Apple and rapid-growth unicorns like Robinhood.

Before starting Cyberbase, they ran a consultancy providing fractional CSO services across Silicon Valley. During that time, they noticed a frustrating pattern: deals constantly "died in the gap." The problem wasn't a lack of actual security; it was a lack of communication.

Legal teams were redlining contracts based on old privacy policies. Sales engineers were answering security questionnaires using expired SOC 2 reports. Reconciling all of this took hundreds of hours. Companies were paying highly skilled security professionals upwards of $130,000 a year just to act as administrative document pushers. McLachlan and Sinkevich realized that existing tools were just treating the symptoms. They built Cyberbase to cure the disease.

The Industry "Trust Problem"

The compliance industry suffered a massive scandal involving fake, automated audit reports. Cyberbase capitalizes on this by shifting the market focus back to real, verifiable security architecture.

The launch of Cyberbase perfectly coincided with an industry-wide crisis of confidence. The founders refer to this as the "Trust Problem." Recently, a major scandal exposed how legacy compliance automation tools were generating "fake compliance."

In one glaring example, investigators found that 493 out of 494 SOC 2 reports generated by a specific platform contained the same typo. Hundreds of other reports claimed zero security incidents and zero personnel changes over an entire year—a statistical impossibility for any real company.

The enterprise market had bought into the "illusion of speed." Buyers wanted compliance in days, not months, which led to rubber-stamp templates that offered zero real protection from regulatory fines. Cyberbase champions a category shift. Instead of generic templates, it blends human expertise with AI to pull evidence directly from a company's live operational environment.

The Context Engine: Not Just Another LLM

Unlike generic AI that hallucinates, Cyberbase's Context Engine maps every internal document into a living web. If a policy changes, every related contract and questionnaire updates instantly.

To make this work, Cyberbase built something called the "Context Engine." Traditional generative AI struggles with complex legal workflows because it relies on static playbooks. If your company updates a data policy, a standard AI won't know unless you manually retrain it.

The Context Engine works differently. It ingests everything—Master Services Agreements, SOC 2 reports, DDQs, and internal policies—and maps the semantic relationships between them.

If your engineering team updates an encryption standard, the Context Engine instantly flags every single downstream contract or public Trust Center page that needs to reflect that change. This allows for "surgical redlining." When the AI suggests an edit to a contract, it’s 100% traceable to your company's actual, current security posture. No hallucinations. No guesswork.

The Pitch: Tailored for the C-Suite

When Jon and Sasha pitch Cyberbase, they don't sell features. They sell business outcomes tailored to the specific anxieties of the executive they are talking to.

• For the VP of Sales: The pitch focuses on culture and velocity. Traditional security training is boring and slows reps down. Cyberbase acts as an invisible safety net, reducing human error without ever putting friction in the sales cycle.
• For the CISO: The pitch focuses on risk mitigation and ROI. It’s about automating the "human firewall." CISOs can stop wasting 20% of their week on manual reporting and get back to actual threat hunting.

Proof in the Data: The Augment Code Benchmark

In just six months, Cyberbase saved Augment Code over 700 hours of manual labor, delivering a 13:1 ROI.

The promises of the Context Engine aren't just theoretical. Look at their deployment with Augment Code, an AI software development firm that relies on complex enterprise sales.

Before Cyberbase, Augment Code's security team was drowning in manual questionnaire responses. After fully deploying the platform for six months, the results were staggering:

• 8,356 individual due diligence questions answered automatically.
• 155 enterprise contracts processed with 2,966 automated redlines.
• 743 human hours saved.

Security responses that used to take three to five days were finished in seconds. Factoring in the cost of specialized talent, those saved hours equated to roughly $185,750 in operational savings against a platform cost of just $14,394—a massive 13:1 ROI.

See how easy contract redlining can be with Cyberbase—try it free today.