Domestic AI Security Agents Break into Global Top 4: Where Are the Opportunities for Entrepreneurs?

Sangfor AI broke into the global top four and ranked first domestically in the CyberGym evaluation, surpassing OpenAI and Anthropic. This achievement, amidst the current large model competition, is worth every entrepreneur reading carefully.

My core judgment is: AI security is not a "moat" exclusive to giants, but an excellent track for differentiated implementation. Especially for technical startup teams, the fragmentation, high average ticket size, and strong customization attributes of security scenarios are precisely good directions to avoid big tech firepower and establish cash flow.

Why Sangfor, and not OpenAI

First, look at the evaluation itself. CyberGym is an industry-recognized AI security benchmark, covering scenarios like vulnerability mining, attack simulation, and defense strategies. Sangfor AI exceeded GPT-4o and Claude 3.5 Opus on multiple metrics. Behind this lie two implications:

1. Security scenarios require domain data. General large models excel in language understanding, but security vulnerability patterns, attack methods, and defense strategies are highly structured professional fields. Sangfor has been deep in cybersecurity for twenty years, accumulating vast amounts of real offense-defense data, security logs, and expert annotations. These data are difficult for general models to obtain.

2. Inference efficiency and cost are key. Security detection requires real-time response; general models cannot meet production environment needs regarding inference latency and cost. What Sangfor likely did was distillation + fine-tuning, using lightweight models deployed locally while maintaining high precision.

[!note] Startup Insight

Don't try to compete with big players on general capabilities. Find vertical scenarios with high data barriers and strong real-time requirements, and dominate them with domain models + engineering optimization.

Three Implementation Suggestions for Entrepreneurs

1. Target the rigid demand market of "Security + Compliance"

AI security isn't just about vulnerability mining. Data compliance, privacy protection, and model security assessments faced by enterprises are all rigid demands. Especially in heavily regulated industries like finance, healthcare, and government affairs, willingness to pay is strong, and decision chains are short. Sangfor's product system covers these clients, but there remains a large gap among SMEs—they need lightweight, subscribable, partially managed AI security services.

What entrepreneurs can do: Build "AI Security SaaS," providing automated vulnerability scanning + compliance report generation for SMEs. You don't need to train your own large models; you can fine-tune based on open-source models (like Llama, Mistral) and hook onto security knowledge bases.

2. Turn Security into a "Service" rather than a "Tool"

The board secretary mentioned "security agents," and the key lies in the word "agent." Traditional security products are tools requiring human operation. AI security agents should be proactive and automated: detect anomalies, analyze automatically, generate repair plans, and even execute directly. This is the form users are willing to pay for.

Product Form Suggestions:

• Adaptive security policies: Automatically adjust firewall rules based on business changes
• Automated penetration testing: Periodically launch simulated attacks on systems and output reports
• Real-time conversational troubleshooting: Ops staff ask in natural language, "Which IP attacked our database yesterday?", and the agent gives the answer directly

3. Team Execution Matters More Than Technology

Leading in evaluations doesn't mean the product sells. Sangfor has sales channels, brand endorsement, and after-sales service. Entrepreneurs need to fill these gaps. My suggestions are:

• Technical partners must understand security, not just models, but also network protocols, system vulnerabilities, and compliance requirements.
• Sales teams must be able to tell scenarios, not just talk about technical parameters, but help customers calculate "how much money is saved by avoiding one data breach."
• Early customer selection: Find 3-5 vertical industries matching Sangfor's client profile, do deep POCs, and polish the product.

Beware of the Fatal Risk of "Large Model Hallucinations" in Security

The error tolerance in the security field is extremely low. If an AI model hallucinates and gives incorrect security advice, it could lead to customer data leaks or system paralysis. This is the trust issue all AI security products must solve.

Implementation Plan:

• Introduce a dual-layer mechanism of "human review + AI suggestion," where critical operations must be confirmed by security experts
• Continuously backtest models with real attack data to establish security baselines
• Provide "model explainability" reports so customers understand why the AI made this judgment

Final Open Question

Is Sangfor's lead here more about engineering and data advantages, or a breakthrough in model architecture itself? If OpenAI or Anthropic start building vertical security models, how can domestic startups hold their ground?

I tend to believe that in the AI security field, the data flywheel matters more than model parameters. Whoever obtains more real offense-defense data first can continuously iterate better models. If startups can deeply bind with several large clients and obtain private data, they have a chance to build a moat.

But reality is, big clients often don't want to share their data externally. Entrepreneurs need to design "data stays within domain" joint training schemes, such as federated learning or differential privacy. This is another dual challenge of technology + business.

Food for thought: If you were an entrepreneur, would you build security SaaS first, or private deployment? Which path is more likely to achieve positive cash flow within 18 months?

Original link: https://www.qbitai.com/2026/07/462447.html