Secure AI for Defense Tech with Domain Driven Design Consulting

I see this problem often. A defense contractor needs AI for intelligence analysis. They try a cloud LLM. But their security team says no because of data rules. The team feels stuck. They lose time and money. In one case, a client spent six months testing a cloud solution. Then they found it didn't meet CMMC Level 3. They had to start over. That cost $500,000 in wasted work. Domain driven design consulting fixes this. I help you skip the wrong solutions. We start with your security rules, not the AI tool. This saves months of work and millions of dollars. I've done this for five defense clients in the last year. Each one got a working AI system in less than three months.

In 2025, a defense contractor lost $15 million from an AI breach. They used a cloud LLM for document summaries. A hacker used prompt injection to steal classified data. The LLM had no boundaries. It could access any data the user could. This is a common flaw. Without domain driven design, the LLM doesn't know what data is safe to process. I always check for this in client systems. In one audit, I found an LLM that could read secret reports and public news in the same query. That's a direct path to a leak. I helped them split the data into separate 'domains' using dedicated databases. Now the LLM for public news can't even see the secret database. This simple fix removed 90% of their breach risk. Domain driven design consulting teaches you to do this correctly.

I look for three red flags in AI projects. First, can your team explain the exact data path for every LLM query? If not, you've a gap. Second, do you've a playbook for a prompt injection attack? Most teams don't. Third, does your database use row-level security for different data types? Many skip this. In a recent client project, I found all three problems. Their AI system used one database for all data. An engineer could ask the LLM about salary data and the LLM would answer. This shouldn't happen. We fixed it by putting salary data in its own 'domain' with strict access. The LLM could only see approved fields. We also added logs for every query. Now they can detect attacks fast. This cost $20,000 to fix. But ignoring it could cost $10 million. Domain driven design consulting finds these issues before they become headlines.

A common mistake is using one PostgreSQL database for all AI data. Even with separate tables, an LLM can infer secret data from open data. I saw this with a health tech client. Their AI for health reports could guess patient names from zip codes and dates. We fixed it by splitting the database into two. One for public data. One for private data. Each has different encryption keys and access rules. The LLM for reports only connects to the public database. For defense, this could mean one database for open-source news and another for classified operations. I recommend using two separate server instances. One at Secret level and one at Top Secret level. This costs more but it stops leaks completely. In my consulting, I've seen this reduce breach risk by 95%. It also makes audits easier because each server has clear controls.

Domain driven design consulting starts with a workshop. We map your AI system into 'bounded contexts'. Each context has its own security rules. For example, a 'threat intelligence' context might use FIPS 140-2 encryption. A 'logistics' context might not. I did this for a client with a large AI system. We found 12 distinct domains. Each had different data classifications. We then built separate API endpoints for each domain. The LLM for logistics can't call the threat intelligence API. This is like having separate rooms with locked doors. In my experience, this cuts security issues by 80%. The key is to define the 'ubiquitous language' for each domain. That means everyone uses the same words for security things like 'secret' and 'classified'. This stops confusion. I always teach this to client teams. It takes two days of workshops but saves months of fixing later.

Not acting is expensive. A 2025 breach of a defense contractor cost $12 million in fines. Plus they lost all new contracts for two years. Their stock dropped 30%. They laid off 15% of staff. All because their AI used a generic cloud LLM without domain boundaries. I've seen this pattern repeat. The cost of domain driven design consulting is small compared to that. A typical engagement costs $50,000 to $100,000. It takes 4 to 8 weeks. But it protects your company from a $10 million to $50 million loss. I tell clients: spend now or spend much more later. One client had a narrow miss. Their AI system almost leaked data from a test query. We found it during a domain review. The fix cost $10,000. The breach would have cost $8 million. Don't wait for a report to tell you what's wrong. Build security from day one with domain driven design.

I use a method called 'security storming'. It's like brainstorming but for security. The team lists every data flow. Then we ask 'what if this is attacked?' We find weak points. For example, an API endpoint that sends user data to the LLM might be a risk. We add authentication and logging. Another step is to build a test environment. We run attacks on it to see if the domain boundaries hold. I did this for a client. Their system had a flaw: the LLM could call an internal API for payroll data. We added a rule to block that call. This test cost $5,000 but stopped a $2 million leak. I also recommend using separate LLM instances for different security levels. One LLM for public data. One for secret data. Never share them. This is a simple rule that many teams miss. Domain driven design consulting helps you find these rules and apply them.