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A reality check on securing generative AI in 2025
So there I was, going through my Databricks GenAI Associate course materials, when I hit the Security and Governance module. You know that feeling when you've heard a term thrown around a hundred times but never really got it? Yeah, that was me with AI security.
But here's what made me sit up straight: the industry has a massive skills gap problem right now.
Think about it. We've got three groups of people, and none of them are fully equipped for what's coming:
Data Scientists → Used to worrying about model accuracy and feature engineering. Now they're being told, "Oh, by the way, you also need to secure all of this."
Machine Learning Engineers → Spent years working with models where you could actually understand what was happening inside. Random forests? Interpretable. Linear regression? Crystal clear. Now? They're dealing with billion-parameter language models that are essentially black boxes.
Security & Governance Pros → These folks know security inside and out. But GenAI? That's a whole new beast they're still learning to tame.
The result? We desperately need more people who can bridge these worlds. And fast.
Here's the thing: we're all using AI platforms constantly. ChatGPT for brainstorming. Claude for coding. Gemini for research. It's become second nature.
But when you're using AI for your organization—where you're feeding it customer data, proprietary code, or strategic documents—suddenly we're playing with very different stakes.
One wrong prompt. One unsecured endpoint. One leaked dataset. That's all it takes.
The good news? The industry isn't sitting idle. Platforms like Databricks have rolled out some seriously smart defenses.
Databricks' Mosaic AI Gateway now includes built-in safety filters that use models like Llama Guard 3 to automatically detect and block harmful content—violence, hate speech, PII leaks, you name it.
The beautiful part? You don't need to write elaborate system prompts saying "pretty please don't say anything violent." The gateway just... handles it. At the infrastructure level.
Speaking of Llama Guard, let me clear up what it actually is (because I was confused at first too).
Llama Guard isn't just one thing—it's Meta's comprehensive framework for AI safety, and it has two critical components:
A Taxonomy of Risks - Meta's safety risk taxonomy that categorizes different types of harmful content:
S1: Violent Crimes
S2: Non-Violent Crimes
S3: Sex-Related Crimes
S4: Child Exploitation
S5: Defamation
S6: Specialized Advice (financial, legal, medical)
S7: Privacy Violations
S8: Intellectual Property
S9: Indiscriminate Weapons
S10: Hate Speech
S11: Suicide & Self-Harm
S12: Sexual Content
S13: Elections
Fine-tuned LLM Classifiers - Specialized models (like Llama Guard 3-8B and 3-11B-Vision) that have been instruction-tuned to detect these categories
This is where it gets tactical. There are two checkpoints where you need protection:
Input Guardrails → Check if the user's prompt is trying something malicious
Returns: safe or unsafe (binary classification)
If unsafe, also returns which category was violated (e.g., "S1: Violent Crimes")
Example malicious inputs: prompt injections, jailbreaks, hate speech
Output Guardrails → Verify the model's response won't cause harm
Same binary check
Catches when your model accidentally generates something problematic
Critical for preventing leaks, hallucinated harmful advice, or generated insecure code
Here's a quick example of what Llama Guard returns:
# Input is unsafe{ "is_safe": False, "violated_categories": ["S1"], # Violent Crimes "explanation": "The prompt requests instructions for violent activities"}# Input is safe{ "is_safe": True, "violated_categories": [],}But here's what really hit me during the course: security can't just be at the prompt level. You need to think about the entire pipeline:
Each stage is a potential vulnerability:
Raw Data → Is it properly access-controlled? Encrypted at rest?
Prepared Data → Are you accidentally baking in PII or biases?
Model → Can someone extract training data through clever queries?
Model Serving → Are your endpoints properly authenticated?
Production → Who can see the inputs and outputs?
MLOps → Is your deployment pipeline secure against supply chain attacks?
This is why platforms like Databricks built Unity Catalog—to provide governance across this entire lifecycle in one place. Row-level security, column masking, audit logging, lineage tracking... all working together.
While Llama Guard gets the spotlight (and for good reason), there's a whole ecosystem of guardrail tools you should know about:
LlamaFirewall → Meta's comprehensive orchestration framework that includes:
PromptGuard 2 - Detects prompt injection and jailbreak attempts
Agent Alignment Checks - Audits agent reasoning to catch goal misalignment
CodeShield - Static analysis to prevent insecure code generation
NVIDIA NeMo Guardrails → Uses a custom language (Colang) to define explicit safety rules and dialog control
Guardrails AI → Focuses on structured output validation using RAIL (Robust AI Language) specifications
Since you're doing the Databricks course (like me), here's what they offer:
Unity Catalog provides:
Fine-grained access control (row & column level)
Automatic PII detection and masking
Comprehensive audit logging
End-to-end lineage tracking
AI asset governance (models, notebooks, functions)
Mosaic AI Gateway adds:
Safety filters using Llama Guard models
Rate limiting per user/service
Request/response logging to Unity Catalog
Integration with external LLMs while maintaining governance
AI Functions like ai_mask() for automatically detecting and masking PII in text data
Security and governance in GenAI isn't optional anymore. It's not something you bolt on at the end. It needs to be designed in from day one.
The good news? The tools exist. Frameworks like Llama Guard, platforms like Databricks with built-in governance, and a growing ecosystem of security-focused solutions.
The bad news? There's still a massive skills gap. The industry needs more people who understand both the AI and the security sides of this equation.
Which is exactly why courses like the Databricks GenAI Associate certification matter. We need to upskill—fast.
Skills Gap Crisis: Most pros are strong in either AI or security, not both
Two Critical Guardrails: Input (check user prompts) and Output (verify model responses)
Llama Guard Framework: Combines a 13-category risk taxonomy with fine-tuned classifiers
Beyond Prompts: Secure every stage from raw data to production
Platform Solutions: Modern platforms like Databricks bake security in at the infrastructure level
Continuous Evolution: This is a moving target—stay updated with latest frameworks and best practices
What's your experience with GenAI security? Drop a comment on reddit and tag Enqurious or reach out—I'd love to hear what challenges you're facing and what's working for your team.
This post was inspired by my journey through the Databricks GenAI Associate certification. All technical details are accurate as of December 2025. For the latest on Llama Guard and related tools, check Meta's Llama Protections page.
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