AI security incidents are climbing. Organisations are rushing to deploy LLMs and generative AI tools, and attackers are keeping pace. You’ve got limited security resources but the pressure to ship AI features isn’t going away. Most of the frameworks out there—NIST, OWASP—assume you have a dedicated security team. You probably don’t.
This article is part of our comprehensive guide to AI safety and interpretability breakthroughs, focused specifically on the practical security tools you need. We’ll cover pre-deployment evaluation, ongoing protection, and vendor assessment. No theory, just security you can implement.
Let’s start with the threat you need to understand first.
What Is Prompt Injection and Why Should Technical Leaders Care?
Prompt injection is a vulnerability that lets attackers manipulate how your LLM behaves by injecting malicious input. It sits at the top of the OWASP LLM Top 10—the most common attack vector against AI systems. For a deeper understanding of how these vulnerabilities emerge from model architecture, see our article on LLM injectivity and privacy risks.
Here’s what makes it different from the vulnerabilities you’re used to. SQL injection and XSS exploit code bugs. Prompt injection exploits how LLMs work. They process instructions and data together without clear separation. That’s the feature that makes them useful. It’s also what makes them exploitable.
The attacks come in two flavours. Direct injection is obvious—someone types “Ignore all previous instructions” into your chatbot. Indirect injection is sneakier: malicious instructions hidden in documents or webpages that your system ingests.
When attacks succeed, the impacts include bypassing safety controls, unauthorised data access and exfiltration, system prompt leakage, and unauthorised actions through connected tools. That means compliance violations, reputation damage, and data breaches.
If you’re relying on third-party AI tools with varying security postures—and most organisations do—your exposure multiplies. One successful attack can compromise customer data or intellectual property. Microsoft calls indirect prompt injection an “inherent risk” of modern LLMs. It’s not a bug. It’s how these systems work.
Traditional application security doesn’t fully address this. You can’t just sanitise inputs like you would for SQL injection. The same natural language that makes LLMs useful makes them exploitable.
What Should Be on Your Pre-Deployment AI Security Checklist?
Before any AI system goes live, run it through this checklist. Start with model and input controls—they’re highest priority—then work down.
Model and Input Controls (Highest Priority)
- [ ] Model provenance verified: source, training data, known vulnerabilities documented
- [ ] System prompts designed with clear role definitions and security constraints
- [ ] Input validation for all inputs: user input, external content, encoded data
- [ ] Character limits set appropriately for your use case
- [ ] Encoding detection and validation in place
- [ ] Structured prompt formats separating instructions from data
Output and Access Controls
- [ ] Output monitoring and validation configured
- [ ] Sensitive data masking implemented
- [ ] Content policy enforcement defined
- [ ] Principle of least privilege applied
- [ ] Authentication and authorisation implemented
- [ ] Rate limiting configured per user and IP
Data and Compliance
- [ ] Data flow documented: what reaches the model, where outputs are stored
- [ ] Data classification system established
- [ ] Compliance requirements identified: SOC2, HIPAA, GDPR as applicable
- [ ] Privacy policies updated for AI data processing
- [ ] Incident response procedures include AI-related scenarios
- [ ] Emergency controls and kill switches in place
- [ ] AI governance framework requirements addressed including ISO 42001 alignment
Resource Estimates: Most teams knock out model and input controls in 1-2 days, output and access controls in another 2-3 days. Data and compliance depends on your existing governance—anywhere from a few hours to several weeks if you’re starting from scratch.
Adding AI expands your attack surface and creates new compliance headaches. Skip items on this checklist and you’re just accepting more risk and creating work for your future self.
Run a pilot test before full integration. Define scope, prepare test data, evaluate security controls in a controlled environment. Finding problems in pilot is a lot cheaper than finding them in production.
How Do LLM Guardrails Protect Against Prompt Injection?
Guardrails are technical safeguards that filter, validate, and control what goes into and comes out of your LLM. Think of them as defence-in-depth—multiple barriers an attacker needs to break through.
Input guardrails detect and block malicious prompts before they reach the model. Strict input validation filters out manipulated inputs—allowlists for accepted patterns, blocklists for attack signatures, anomaly detection for suspicious behaviour.
Output guardrails filter responses before they reach users, catching data leakage and policy violations. Content moderation tools scan outputs automatically based on rules you define.
You’ve got options. Regex rules and pattern matching are simple and fast but easily bypassed. ML-based classifiers are more robust but need tuning. Purpose-built frameworks sit in between.
For tools, NeMo Guardrails works well for conversational AI, and moderation models like Llama Guard give you ready-made classifiers.
Microsoft layers multiple safeguards: hardened system prompts, Spotlighting to isolate untrusted inputs, detection tools like Prompt Shields, and impact mitigation through data governance. You probably don’t need all of that, but the principle of layering is worth adopting.
Here’s the trade-off: stronger guardrails mean more latency and potentially degraded user experience. Too strict and users get frustrated. Too loose and attacks get through. Test with input fuzzing to see how your system handles unusual inputs, then adjust accordingly.
For agent-specific applications—where your LLM is calling tools or taking actions—you need tighter controls. Validate tool calls against user permissions, implement parameter validation per tool, and restrict tool access to what’s actually needed. If your model doesn’t need to send emails, don’t give it access to the email API.
What Questions Should You Ask When Evaluating AI Vendor Security?
When you’re buying AI tools, use this questionnaire for procurement.
Data Handling
- Where is data processed and stored? (Vague answers are a red flag)
- How long is data retained?
- Can vendor employees access your prompts and outputs?
- What happens to your data if you terminate the contract?
Security Certifications
- SOC2 Type II? (Type II covers ongoing controls—better than Type I)
- ISO 27001?
- Industry-specific compliance?
Check vendor cybersecurity posture through certifications and audits. Ask to see reports, not just claims.
Incident Response and Transparency
- SLAs for breach notification?
- Can you conduct or commission security assessments?
- Which subprocessors have access to your data?
- What safeguards are built in, what can you configure?
Due diligence for AI vendors covers concerns like data leakage, model poisoning, bias, and explainability. These aren’t traditional IT security questions, but they matter for AI systems.
Don’t just accept answers at face value. Run a Pilot or Proof-of-Concept and ask for customer references in your industry. Selecting a vendor is a partnership—negotiate security terms into contracts. If a vendor won’t commit to security requirements in writing, that tells you everything you need to know.
How Do You Conduct AI Red Teaming for Prompt Injection Vulnerabilities?
Red teaming is adversarial testing to find vulnerabilities before attackers do. You’re deliberately trying to break your own systems.
Scope and Attack Scenarios
Decide what you’re testing and what counts as success. For prompt injection, success might mean exfiltrating data, bypassing content filters, or getting the model to ignore its system prompt.
Test cases should cover direct injection, indirect injection (malicious content in documents), jailbreaking, data extraction, and typoglycemia attacks.
Make sure your red team exercises include edge cases and high-risk scenarios. Test abnormal inputs. Find blind spots.
Tools
Manual testing finds weird edge cases. Automated scanning covers volume. Most teams use both. Garak is an LLM vulnerability scanner. Adversarial Robustness Toolbox and CleverHans are open-source defence tools. MITRE ATLAS documents over 130 adversarial techniques as a reference for attack patterns. For organisations wanting to understand the technical verification methods underlying these tools, circuit-based reasoning verification offers deeper insight into model behaviour.
Google’s approach includes rigorous testing through manual and automated red teams. Microsoft recently ran a public Adaptive Prompt Injection Challenge with over 800 participants.
Build vs Buy
Start with external specialists. They establish baselines and bring experience from multiple engagements. Build internal capability gradually if you’ve got ongoing AI development. A hybrid model works well: internal teams for routine testing, external specialists for periodic deep assessments.
Benchmark against standard adversarial attacks to compare with industry peers. Document findings with severity ratings and remediation recommendations, then integrate into your development workflow. Red teaming only helps if you fix what it finds.
What Ongoing Monitoring Should You Implement for AI Security?
Security doesn’t end at deployment. You need continuous visibility.
Input and Output Monitoring
Track prompt patterns and flag anomalies. Log all responses. Alert on policy violations and potential data leakage. Implement rate limiting, log every interaction, set up alerts for suspicious patterns.
Performance and Alerts
Establish baselines so you can spot deviations. The core pillars are Metrics, Logs, and Traces—measuring KPIs, recording events, and analysing request flows.
Balance alert sensitivity with noise. Too many alerts and your team ignores them. Build playbooks for common scenarios like spikes in guardrail triggers.
Audit and Compliance
Set up automated audit trails with complete logging of AI decisions. Give users flagging capabilities to report concerning outputs—when a prompt generates responses containing sensitive information, they can flag it for review. Track guardrail triggers, blocked requests, and latency impact.
A SOC approach works even at smaller scale. The four processes are Triage, Analysis, Response and Recovery, and Lessons Learned. You don’t need a dedicated SOC—you need the processes.
How Do You Train Your Team on AI Security Best Practices?
Tools and processes only work if your people know how to use them.
Why Training Matters
GenAI will require 80% of the engineering workforce to upskill through 2027 according to Gartner. Teams without proper training see minimal benefits from AI tools. Same goes for security—giving people guardrail tools without teaching them to configure and maintain them doesn’t help.
Role-Based Training
Different roles need different depth. All staff need awareness of AI risks. Developers need secure coding and advanced techniques like meta-prompting and prompt chaining. Security teams need threat detection and guardrail configuration.
DevSecOps should be shared responsibility—security defines strategy, development implements controls. Establish Security Champions within your engineering teams.
Content and Maintenance
Cover AI basics, ethical considerations, and practical applications. Include prompt injection labs where people try to break systems, guardrail configuration exercises, and incident simulations. Senior developers need clear guidance on approved tools and data-sharing policies.
Run regular security audits of AI-generated code to identify patterns that might indicate data leakage or security vulnerabilities. Train developers to recognise these patterns.
AI security evolves fast. Measure effectiveness through assessments, reduced incidents, and faster response times. If incidents aren’t dropping, your training isn’t working.
FAQ Section
That covers the main areas. Here are answers to questions that come up often.
What’s the difference between AI safety and AI security?
AI safety ensures systems behave as intended without unintended harm. AI security protects against malicious attacks and misuse. You need both, but security specifically addresses adversarial threats—people actively trying to break your systems.
Can open-source LLM guardrail tools provide adequate protection?
Yes. NeMo Guardrails, Llama Guard, and LLM Guard provide solid baseline protection for many use cases. They require more configuration than commercial solutions. Evaluate based on your team’s capacity to maintain them.
How much should an SMB budget for AI security?
Start with 10-15% of AI implementation costs. For applications handling sensitive data, consider 20% or more. Factor in ongoing monitoring and training, not just setup.
Should we build red teaming capability internally or hire external specialists?
Start external. Specialists bring experience from multiple engagements. Build internal capability gradually if you’ve got ongoing AI development. A hybrid model works well: internal for routine testing, external for periodic deep assessments.
What’s the minimum viable AI security programme for an SMB?
Input and output safeguards on all AI applications. Vendor security questionnaire. Basic monitoring and logging. Incident response procedure. Annual training. That’s your foundation—it expands as AI usage grows.
How do we know if our guardrails are actually working?
Test them regularly with known payloads. Monitor trigger rates—too few may indicate gaps, too many means over-blocking. Conduct periodic red team exercises.
What compliance frameworks specifically address AI security?
The NIST AI Risk Management Framework provides comprehensive guidance with four core functions: GOVERN, MAP, MEASURE, and MANAGE. OWASP LLM Top 10 catalogues threats. Industry frameworks like HIPAA and SOC2 apply to AI systems processing relevant data. The EU AI Act introduces requirements by risk category.
How do we handle AI security incidents differently from traditional incidents?
AI incidents may require model rollback rather than code patches. You need prompt analysis to understand attack vectors. Recovery may involve retraining. Logs must capture prompts and outputs. Response teams need AI-specific expertise.
Is it safe to use AI tools that process customer data?
With proper controls, yes. Verify vendor data handling, ensure contractual protections, implement safeguards, anonymise sensitive data, maintain audit trails. Risk level depends on data sensitivity and vendor security posture.
How often should we review and update our AI security controls?
Quarterly at minimum. Update immediately when new vulnerability classes are discovered. Reassess whenever you deploy new AI capabilities.
