At DCX, we help businesses adopt AI in a way that is actually usable in the real world: integrated into your systems, measurable, supportable, and secure by design.

I got into AI the same way a lot of people do: curiosity. I watched the demos, spoke with experts, and started building real solutions. That is when it became obvious that security is the make-or-break factor, especially once you move beyond chat and start integrating AI into workflows, email, documents, and line-of-business apps.

A lot of developers have rushed into the AI goldmine and unintentionally introduced new risk into organizations worldwide. The most confronting lesson for me came while building a solution for a family member: the AI kept choosing the easiest way to make it work, even when that meant side-stepping security best practice. For example, it tried to solve exposed keys by splitting them up or hiding them with Base64, which is not real security. That was the moment I stopped, reviewed the entire approach, and treated AI like any other powerful system component: it must be controlled, constrained, and verified.

The truth is: AI is evolving fast, toolchains are changing weekly, and it is easy for organizations to lose track of what is safe. And if an attacker realizes you are using AI (agents, copilots, automations, RAG search, plug-ins), they will often probe it as the weakest link.

Common ways attackers target AI systems

These are the most common AI hacking methods we plan for and defend against, described in plain English and not as a how-to guide. Many align closely with the OWASP LLM Top 10 risks.

1. Prompt injection (direct and indirect)
   Attackers craft instructions that try to override what the AI should do or hide those instructions inside content the AI reads (web pages, emails, documents). This is a major real-world risk for AI agents and copilots.
2. Data leakage and sensitive information disclosure
   If an AI can see internal content (SharePoint, mailboxes, CRMs), attackers may try to coax it into revealing data it should not - sometimes through clever questions, sometimes through injected instructions, sometimes through permission mistakes.
3. Insecure output handling (AI output becomes an attack path)
   If AI-generated output is blindly trusted (fed into scripts, databases, ticketing systems, or admin tools), attackers can use the AI as a stepping-stone into downstream systems. This is why validation and guardrails matter.
4. Over-permissioned agents and tool abuse
   When an AI agent can do things (send emails, edit files, run actions), the risk becomes less about the model and more about permissions. If it has broad access, attackers only need one gap to make it act unsafely.
5. RAG or knowledge-base poisoning
   If your AI uses a knowledge base (docs, wikis, websites) to answer questions, attackers can try to seed misleading or malicious content so the AI retrieves and follows it.
6. Supply chain vulnerabilities
   AI systems depend on libraries, plug-ins, integrations, APIs, and hosted services. A compromise in any dependency can become your compromise.
7. Training or fine-tuning data poisoning
   If you train or fine-tune models, poisoned data can intentionally skew behavior, weaken safety, or introduce subtle backdoors.
8. Model denial of service and cost blowouts
   Attackers can intentionally trigger expensive workloads (huge prompts, repeated calls, heavy tool use) to degrade performance or inflate cloud spend.

How DCX delivers secure AI integrations

We do not bolt on AI. We implement it with the same discipline you would expect for identity, email security, or network segmentation, because the risk is comparable.

Our security-first approach typically includes:

• Threat modelling for AI workflows (what can it access, what can it do, what happens if it is tricked) using established AI risk guidance.
• Least-privilege access: the AI only gets the minimum required permissions and nothing admin by default.
• Human-in-the-loop controls for high-impact actions (sending external email, updating financial records, deleting data).
• Guardrails and policy: strong system prompts, constrained tools, allow-lists, content filtering, and safe fallbacks.
• Output validation: AI output is treated as untrusted input until it is validated and sanitized.
• Logging, auditing, and monitoring so you can prove what happened and respond fast.
• Red-teaming and testing for prompt injection and data leakage behaviors, not just does it work.
• Ongoing change management because AI platforms and models shift fast and security baselines must be reviewed regularly.

What we integrate AI into

Depending on your environment, DCX can implement AI across:

• Microsoft 365 (mailboxes, SharePoint, Teams workflows)
• CRMs and ERPs (lead capture, triage, summarization, next-step suggestions)
• Service desks (ticket enrichment, categorization, suggested responses)
• Document pipelines (extraction, classification, searchable knowledge bases)
• Website chat and customer support flows
• Internal knowledge assistants (with strict access control)

A practical, safe rollout path

1. Discovery and risk review - identify workflows worth automating and what data is involved.
2. Security architecture - permissions, boundaries, logging, and compliance needs.
3. Prototype - a controlled pilot with safe data scope.
4. Production integration - monitoring, governance, and support model.
5. Continuous improvement - tuning and security reviews as tools evolve.