Open Source AI: Benefits, Risks & DeepSeek’s Rise

In early 2025, a single model release changed how the world talks about artificial intelligence. When DeepSeek shipped a frontier-level reasoning model, open source AI stopped being a niche research topic and became a boardroom conversation.

Open source AI means an AI system you are free to use, study, modify, and share. Instead of renting intelligence from a closed vendor, organizations can download a model and run it themselves.

This guide explains what open source AI really is, the genuine benefits driving its adoption, and the security risks that come with it. At CyberX, we help organizations adopt new technology without opening new doors for attackers.

What Is Open Source AI?

The phrase gets used loosely, so it helps to be precise. According to the Open Source Initiative, as summarized on Wikipedia, true open source AI makes the model freely available to use, study, modify, and share.

That ideally includes three things:

• The training data used to build the model.
• The code that runs and trains it.
• The model weights (the trained parameters).

Open Source vs. Open Weights vs. Closed Models

Not every “open” model is equally open. The distinctions matter for security and compliance.

• Closed models — you access them only through an API. The vendor controls everything (for example, GPT-5 or Claude).
• Open-weights models — the weights are released, but the training data and full code may not be. Many popular models fall here.
• Fully open source AI — data, code, and weights are all published.

Releasing only weights while calling a model “open source” has been criticized as openwashing — marketing something as open when it is mostly closed.

DeepSeek’s Breakthrough Moment

DeepSeek mattered because it proved open source AI could compete at the frontier, not just in the lab. It showed that a freely downloadable model could rival expensive closed systems.

That shift emboldened other open efforts and triggered a surge in adoption. According to NIST’s 2025 CAISI evaluation, downloads of DeepSeek models rose nearly 1,000% between January and mid-2025.

The Benefits of Open Source AI

The excitement is not just hype. Open source AI delivers real, practical advantages, especially for organizations that value control.

Lower Cost and No Vendor Lock-In

Running your own model changes the economics of AI.

• No per-query API fees for high-volume workloads.
• Freedom to switch or fine-tune without a vendor’s permission.
• No dependence on a single provider’s pricing or roadmap.

Data Control and On-Premise Deployment

For many organizations, this is the decisive benefit of open source AI.

• Queries run on machines you control, not a third party’s servers.
• Sensitive data never has to leave your environment.
• You decide what is logged, stored, and deleted.

Why Regulated Sectors Care

Banks, healthcare providers, and government bodies operate under strict data-residency rules.

• Keeping data in-house helps meet regulations like Saudi Arabia’s PDPL and the GDPR.
• On-premise AI reduces the risk of sensitive records leaking to an external provider.

Transparency and Third-Party Review

Open models can be inspected, which closed ones cannot.

• Independent security teams can red-team the model directly.
• Vulnerabilities can be studied openly rather than hidden.
• Organizations can verify behavior instead of trusting a vendor’s word.

The Security Risks of Open Source AI

Openness cuts both ways. The same freedom that helps defenders also helps attackers, and the evidence here is concrete.

Jailbreaks and Prompt Injection

Many open models are weaker at resisting manipulation than leading closed systems.

NIST’s evaluation found DeepSeek’s most secure model answered 94% of overtly malicious requests when a common jailbreak was used, compared with just 8% for U.S. reference models.

• Jailbreaking tricks a model into ignoring its safety rules.
• Prompt injection hides malicious instructions inside ordinary-looking input.

Weaponization — Local, Unfiltered Models

Because open source AI can run locally, attackers can strip away the guardrails entirely.

• No rate limiting, content filtering, or activity monitoring.
• Models can be wired into automated attack tools.

NIST found agents built on DeepSeek’s model were, on average, 12 times more likely to follow malicious hijacking instructions than U.S. frontier models — in tests, hijacked agents sent phishing emails and even downloaded malware.

Supply-Chain Risks and Hidden Backdoors

An open model is a software component, and it carries software-supply-chain risk.

• Models may ship with outdated or vulnerable dependencies.
• Malicious actors can publish tampered models on sharing platforms.
• A hidden backdoor scales to everyone who downloads the model.

This is the same discipline you apply to any untrusted download — the caution that also protects you from ransomware and trojanized software.

Open Source AI in Cybersecurity — A Double-Edged Sword

Nowhere is the trade-off sharper than in security itself, where open source AI is used by both sides.

How Defenders Use It

• Analyzing huge volumes of logs to spot anomalies.
• Automating threat detection and triage.
• Running private models so sensitive incident data stays in-house.

For a deeper look at using AI safely in a regional context, see our article on navigating AI security in digital Saudi Arabia.

How Attackers Abuse It

• Generating convincing phishing and social-engineering content at scale.
• Writing or refining malicious code.
• Automating reconnaissance against targets.

The lesson is not to avoid AI, but to understand it. That understanding is the foundation of modern cybersecurity.

How to Adopt Open Source AI Safely

You do not have to choose between innovation and safety. A disciplined process lets you capture the benefits while containing the risks.

A Practical Vetting Checklist

1. Verify the source. Download models only from official, reputable repositories.
2. Check the license and provenance. Know who built the model and what it was trained on.
3. Scan dependencies. Treat the model like any third-party software component.
4. Red-team before deployment. Test for jailbreaks and prompt injection.
5. Deploy with guardrails. Add content filtering, logging, and rate limiting yourself.
6. Isolate sensitive workloads. Keep high-risk data on tightly controlled systems.
7. Train your people. The users interacting with the model are your last line of defense.

Conclusion

Open source AI is one of the most important shifts in modern technology. It lowers costs, protects data, and puts control back in the hands of the organizations that use it.

But openness is not the same as safety. The same models that empower defenders can be stripped down and weaponized by attackers, as NIST’s 2025 findings make plain.

The organizations that win with open source AI will be the ones that treat it like any powerful tool: adopt it deliberately, secure it thoroughly, and train the people who use it.

Adopt AI Safely With CyberX

New technology only creates value when your people can use it without becoming the weak link. Open source AI raises the stakes, because a single careless prompt or unvetted download can expose your whole environment.

AwareX, our security awareness training platform, trains teams to use AI tools responsibly — spotting AI-powered phishing, avoiding risky downloads, and protecting sensitive data. It turns AI anxiety into practical, everyday safe behavior.

Want to roll out AI without opening new risks? Contact the CyberX team to book an AwareX demo.

Frequently Asked Questions

What is open source AI? Open source AI is an AI system that is freely available to use, study, modify, and share. In its fullest form it includes the training data, the code, and the model weights, though many “open” models only release the weights.

Is DeepSeek safe to use? DeepSeek made open source AI competitive, but NIST’s 2025 evaluation found it far more vulnerable to jailbreaking and agent hijacking than leading U.S. models. It can be used, but only with strong external guardrails and careful data handling.

What is the difference between open source and open weights? Fully open source AI publishes data, code, and weights. Open-weights models release only the trained parameters, keeping the data and full code private. Calling the latter “open source” is often criticized as openwashing.

Is open source AI secure for businesses? It can be, if adopted carefully. Download from trusted sources, scan dependencies, red-team the model, add your own guardrails, and train staff. The benefit is data control; the risk is weaker built-in safety.