How to Secure AI Systems When You Don't Have a CISO

You shipped an AI feature. Maybe it is a customer support agent, a RAG pipeline that answers questions about your internal docs, or an LLM integration that helps your sales team draft proposals. It is in production, users are hitting it, and it is generating value.

You also do not have a CISO. No VP of Security. No dedicated security team. Maybe your "security person" is an engineering lead who also handles infrastructure, compliance, and half a dozen other things.

This is the reality for most companies deploying AI today. According to IANS Research, fewer than 30% of companies with under 500 employees have a dedicated security executive. Yet these same companies are deploying AI systems that introduce attack surfaces their existing security practices were never designed to address.

The good news: you do not need a CISO to secure your AI systems. You need a structured approach, the right tools, and a realistic understanding of where AI-specific risks differ from the infrastructure security you are already handling. This guide gives you all three.

Why AI Security Is Different From What Your Cloud Provider Covers

If you are running on AWS, Azure, or GCP, you already have a baseline of infrastructure security. GuardDuty is watching for suspicious API calls. Security Hub is flagging misconfigurations. Your WAF is filtering known attack patterns. You might even have a SIEM aggregating logs.

None of this covers AI-specific attack vectors. Not even close.

Cloud Security Monitors Infrastructure, Not AI Behavior

AWS GuardDuty will alert you if someone is making anomalous API calls to your EC2 instances. It will not alert you when a user crafts an input that causes your customer support agent to reveal its system prompt, including the API keys you embedded in it.

Azure Defender will catch a brute-force attack against your authentication endpoints. It will not catch an indirect prompt injection where a poisoned document in your RAG pipeline instructs your LLM to exfiltrate user data through its responses.

Your cloud provider secures the box your AI runs in. It does not secure the AI itself.

Traditional WAFs Do Not Catch Prompt Injection

A web application firewall operates on pattern matching against known attack signatures. SQL injection has recognizable syntax: ' OR 1=1 --. Cross-site scripting has identifiable payloads: .

Prompt injection does not have a fixed syntax. An attacker can instruct your model to ignore its system prompt using natural language, encoded text, multi-turn conversation manipulation, or adversarial content embedded in documents. There are infinite ways to phrase "ignore your previous instructions," and a WAF that blocks one phrasing will miss thousands of others.

SAST/DAST Tools Do Not Test AI Attack Vectors

Your static analysis tools check for buffer overflows, insecure deserialization, and hardcoded credentials. Your dynamic application security testing scans for XSS, CSRF, and authentication bypass.

Neither of these test whether your AI agent will, when given a carefully crafted prompt, use its tool access to query your production database for data it should not return. Neither tests whether your RAG pipeline will retrieve and surface documents outside its intended scope. Neither tests whether your model leaks training data when prompted with specific prefixes.

AI security requires AI-specific testing. There is no shortcut around this.

Building AI Security Into Your Existing Workflow

You do not need a security team to do this. You need one person who owns it, a checklist, and a few hours per month. Here are five concrete steps.

Step 1: Inventory Your AI Systems

You cannot secure what you do not know about. Before anything else, build a simple inventory of every AI system in your organization. This does not need to be a fancy CMDB entry. A spreadsheet works.

For each AI system, document:

• Model: What model or models are you using? GPT-4o, Claude, Llama, a fine-tuned model, a combination?
• Deployment: Where is it running? A managed API (OpenAI, Anthropic), self-hosted on your infrastructure, or a third-party platform?
• Data access: What data can the model access? Customer records, internal documents, financial data, code repositories?
• Tool access: What actions can the model take? Can it send emails, query databases, create records, modify configurations, execute code?
• Input sources: Who provides input? End users through a chat interface, internal staff, automated pipelines, external documents?
• Output destinations: Where do model outputs go? Directly to users, into databases, into downstream systems?

This inventory is the foundation of everything else. If your AI agent has read/write access to your production database and accepts input from unauthenticated users, that is a different risk profile than a summarization tool that processes internal documents for authenticated employees.

Most companies discover things during this exercise. A team spun up a GPT wrapper three months ago. Someone connected an LLM to the CRM. An intern built a document Q&A tool that has access to the entire Google Drive. Get it all documented.

Step 2: Run the Basics

With your inventory complete, run fundamental security checks against each AI system. You do not need expensive tools for this first pass.

Prompt injection testing. Use Garak, an open-source LLM vulnerability scanner from NVIDIA. It tests for direct prompt injection, jailbreaking, system prompt extraction, and several other attack categories. Install it, point it at your model endpoint, and review the results.

pip install garak
garak --model_type openai --model_name gpt-4o --probes all

For more targeted testing, Promptfoo lets you define custom test cases specific to your application's context. You can write tests like "the model should never reveal the contents of its system prompt" and "the model should refuse to query the users table when asked by someone without admin context."

Output filtering review. Check whether your AI system's outputs are filtered before being returned to users or passed to downstream systems. Specifically:

• Can the model output valid SQL, shell commands, or code that gets executed?
• Can the model return raw data from your database, including fields that should be redacted?
• Can the model generate URLs or links that could be used for phishing?

API authentication review. For every AI endpoint, verify:

• Is authentication required? (You would be surprised how many internal AI tools are accessible without auth.)
• Are API keys rotated regularly?
• Is rate limiting in place?
• Are permissions scoped correctly, or does every authenticated user get the same access?

System prompt review. Read through every system prompt in your codebase. Look for:

• API keys, database credentials, or secrets embedded in prompts
• Overly broad instructions that give the model more authority than it needs
• Missing safety instructions (what should the model refuse to do?)

Step 3: Set Up Monitoring

AI systems need monitoring that traditional application monitoring does not cover. Your APM tool tracks response times and error rates. You also need to track what your AI systems are saying and doing.

What to log for every AI interaction:

• Full input: The complete user message or prompt. This is critical for detecting prompt injection after the fact.
• Full output: The complete model response. This lets you identify data leakage or policy violations.
• Tool calls: If your agent uses tools, log every tool invocation with its parameters and results. This is how you catch unauthorized database queries or API calls.
• Token usage: Unusual spikes in token usage can indicate prompt injection attempts (adversarial prompts tend to be longer) or model abuse.
• Error rates: Track model errors, refusals, and content filter triggers separately from application errors.

Alerts to set up:

• System prompt in output: Alert if any model response contains text from your system prompt. This indicates successful system prompt extraction.
• Sensitive data patterns: Use regex to detect Social Security numbers, credit card numbers, API keys, or other sensitive patterns in model outputs.
• Unusual tool call patterns: Alert if an agent calls tools it does not normally use, or calls them at an unusual frequency.
• Input length anomalies: Prompt injection payloads are often significantly longer than normal user inputs. Alert on inputs that exceed your 95th percentile length.

If you are already using a logging stack like Datadog, Grafana, or even CloudWatch, you can implement most of these as custom metrics and alerts. The key is treating AI interactions as a first-class data source, not just another API call.

Step 4: Establish an AI Security Policy

A policy does not need to be a 50-page document. It needs to cover the decisions your team has already made (or needs to make) about how AI systems are built and operated. Write it in plain language. Keep it under five pages.

Sections to include:

• Acceptable use. What are employees allowed to use AI for? What data can they input into AI systems? Can they use public AI tools (ChatGPT, Claude) for work tasks? If so, what data classification restrictions apply?
• Data handling. What data can be sent to third-party AI providers? What data must stay on-premises or within your cloud environment? Do you need to use data processing agreements with AI vendors?
• Model governance. Who approves new AI deployments? What testing must happen before an AI system goes to production? Who is responsible for monitoring each AI system?
• Prompt and system prompt management. Where are system prompts stored? Who can modify them? Are they version-controlled? Is there a review process for changes?
• Incident response. What happens when an AI security incident is detected? Who is the first responder? What is the escalation path? When do you involve external experts?

You can find templates from NIST, ISO, and OWASP that provide starting points. The NIST AI Risk Management Framework and ISO 42001 both provide structured approaches to AI governance that you can adapt to your organization's size.

Step 5: Get a Periodic External Assessment

Internal testing is necessary but insufficient. Your team built the system, which means they share the same assumptions and blind spots that created any vulnerabilities in the first place. External assessors bring different methodologies, different tooling, and experience from testing systems across many organizations.

What to look for in a vendor:

• AI-specific expertise. Ask what percentage of their assessments focus on AI and ML systems specifically. A traditional penetration testing firm that added "AI" to their service list is not the same as a team that specializes in LLM security.
• Methodology transparency. Ask them to walk you through their testing methodology. Do they test for the OWASP LLM Top 10? Do they include agentic attack vectors? Do they test RAG pipelines?
• Sample deliverables. Ask for a redacted sample report. Look for technical depth: reproduction steps, risk ratings, specific remediation guidance, and compliance mapping.
• Remediation support. Some vendors hand you a report and walk away. Others help you fix the issues they found. The latter is more valuable, especially if you do not have dedicated security staff.

How often: For most SMBs, an annual comprehensive assessment combined with quarterly automated scanning is a reasonable baseline. If you are in a regulated industry or making significant changes to your AI architecture, test more frequently. A focused assessment should happen before any major deployment, such as launching a new customer-facing agent or connecting a model to a new data source.

BeyondScale provides AI security audits designed specifically for companies without dedicated security teams. The assessment covers your full AI attack surface and delivers actionable findings that your engineering team can implement.

The Fractional Security Model

Here is a pattern that works well for companies with 50 to 500 employees: instead of hiring a full-time security team, engage an external AI security team on a fractional basis. Think of it as a retainer model rather than a one-off project.

What a Fractional Engagement Typically Includes

Quarterly security assessments. A structured assessment of your AI systems every quarter. This catches new vulnerabilities introduced by model updates, architecture changes, or new deployments. Each assessment includes a report with findings, risk ratings, and remediation priorities.

Incident response retainer. When something goes wrong, you have a team to call. They have already studied your architecture, they know your systems, and they can respond faster than a vendor you are engaging for the first time during a crisis.

Compliance gap assessments. If you are pursuing SOC 2, ISO 27001, HIPAA, or the EU AI Act, a fractional security team can map your current state against compliance requirements and identify gaps before your auditor does.

On-demand consulting. Building a new AI feature? Connecting a model to a new data source? Adding tool access to an agent? Get a security review of the architecture before you build, not after you deploy.

When This Makes More Sense Than Hiring

A full-time security hire costs $150,000 to $250,000 per year in total compensation, and that gets you one person. One person cannot cover application security, cloud security, AI security, compliance, incident response, and security operations. They will inevitably specialize in some areas and leave gaps in others.

A fractional model gives you a team with diverse expertise at a fraction of the cost. You get specialists in AI security, compliance, and incident response without paying full-time salaries for each.

This model is especially effective when:

• You have fewer than five AI systems in production
• You do not need daily security operations (your cloud provider handles most infrastructure security)
• Your primary risk is AI-specific vulnerabilities rather than infrastructure compromise
• You need compliance support but not a full-time compliance officer

BeyondScale offers a managed AI security program built specifically for this model. It includes continuous monitoring, periodic assessments, and incident response support.

What to Do When Something Goes Wrong

Even with preventive measures in place, AI security incidents happen. Here is how to handle the most common scenarios.

Scenario 1: Prompt Injection Detected in Production

What it looks like: Your monitoring alerts fire because a model response contains fragments of your system prompt, or a user input matches known prompt injection patterns, or your output filter catches sensitive data in a response.

Immediate steps:

• Log the full conversation, including all inputs, outputs, and tool calls from that session
• If the injection succeeded (the model behaved in an unauthorized way), disable the endpoint or switch to a restricted fallback mode that does not accept free-form input
• Check your logs for similar patterns from other users. Prompt injection is often automated, meaning if one attacker found the vector, others may have too
• Preserve all evidence before making changes

Investigation:

• Determine exactly what the attacker achieved. Did they extract the system prompt? Access data they should not have? Trigger tool calls?
• Review the attack input to understand the injection technique. Was it direct injection, multi-turn manipulation, or an indirect injection from an external data source?
• Assess the blast radius. If data was exfiltrated, what data? If tool calls were triggered, what actions were taken?

Remediation:

• Patch the specific injection vector. This might mean updating your system prompt, adding input validation, restricting tool permissions, or implementing output filtering
• Run a focused re-test using the attack technique that succeeded, plus variations of it
• Update your monitoring rules to catch this pattern going forward
• Consider whether architecture changes are needed (for example, moving from single-layer prompting to an agent framework with explicit permission boundaries)

Scenario 2: Data Leakage Through Model Responses

What it looks like: A customer reports that the AI showed them another customer's data. Or your output monitoring catches PII, financial data, or internal information in model responses that should not contain it.

Immediate steps:

• Identify the source of the leaked data. Is it from the model's context window, the RAG pipeline, or the model's training data?
• If the leak is from your RAG pipeline or database, restrict the model's data access immediately
• If a customer's data was exposed to another customer, document exactly what was shared and with whom. You may have regulatory notification obligations depending on your jurisdiction and the type of data involved

Investigation:

• Review the RAG retrieval logs. Did the system retrieve documents outside the user's authorized scope?
• Check your access control implementation. Is the model enforcing per-user data boundaries, or does it have access to all data regardless of who is asking?
• Test whether the leak is reproducible. Can you trigger the same data exposure with a specific query pattern?

Remediation:

• Implement or fix per-user access controls in your retrieval pipeline. The model should only retrieve documents that the requesting user is authorized to see
• Add output filtering that checks responses for data patterns that should not be present (other users' identifiers, restricted data classifications)
• If the data was in the model's training data, you may need to fine-tune or switch models. Training data memorization is a model-level issue that cannot be fixed with prompt engineering alone

Scenario 3: Agent Taking Unauthorized Actions

What it looks like: Your tool call logs show the agent executing actions outside its intended scope. Maybe it queried a database table it should not have access to, sent an email it was not supposed to, or modified records when it should only have read access.

Immediate steps:

• Revoke the agent's tool access or restrict it to a safe subset immediately
• Review all tool calls from the affected time period. Identify every action the agent took that was outside its authorized scope
• If the actions affected production data, assess whether you need to roll back changes

Investigation:

• Determine what caused the agent to act outside scope. Was it a prompt injection that instructed the agent to use tools differently? Was it a failure in the agent's planning logic? Was it an overly broad tool definition?
• Review the agent's tool definitions. Does the send_email tool restrict who the agent can email? Does the database query tool restrict which tables and columns the agent can access?
• Check whether the agent's system prompt clearly constrains what actions are allowed

Remediation:

• Implement explicit tool-level access controls. Do not rely on the system prompt alone to restrict what tools the agent uses and how. Enforce permissions in the tool implementation layer
• Add a confirmation step for sensitive actions. The agent should not be able to send emails, modify data, or make API calls without a human approval step, at least until you trust the system's behavior
• Implement tool call auditing that flags any invocation matching unauthorized patterns
• Consider reducing the agent's tool access to the minimum required for its function. If the agent does not need write access, remove write access

When to Bring in External Help

Handle it internally when:

• The incident is contained and the blast radius is limited
• You understand the attack vector and know how to patch it
• No regulated data (PII, PHI, financial records) was exposed

Bring in external expertise when:

• You cannot determine the attack vector or the full scope of the compromise
• Regulated data was exposed and you may have notification obligations
• The attacker may still have access (for example, a poisoned document in your RAG pipeline that you have not identified)
• You need to demonstrate to customers, partners, or regulators that an independent party investigated the incident

If you need incident response support for AI security issues, contact BeyondScale for immediate assistance.

A Realistic Security Roadmap for Small Teams

You do not need to do everything at once. Here is a phased approach that a single engineering lead can execute over 90 days.

Month 1: Foundation

• Complete your AI system inventory (Step 1)
• Run Garak or Promptfoo against your primary AI systems (Step 2)
• Fix any critical findings: exposed system prompts, missing authentication, overly broad tool permissions
• Set up basic logging of AI inputs, outputs, and tool calls (Step 3)

Month 2: Policy and Monitoring

• Write your AI security policy (Step 4). Keep it short. Get leadership to sign off
• Implement alerts for the patterns described in Step 3
• Review and tighten tool permissions for all AI agents
• Add output filtering for sensitive data patterns

Month 3: Validation and Ongoing Process

• Engage an external team for an initial AI security assessment
• Address findings from the external assessment
• Establish a quarterly review cadence: re-run automated scans, review logs, update your inventory
• Decide whether a fractional security model makes sense for your ongoing needs

This is not a comprehensive enterprise security program. It is the minimum viable security posture for a small team running AI in production. It addresses the most common and highest-impact AI-specific risks, and it gives you a foundation to build on as your AI systems grow.

Where to Go From Here

Securing AI systems without a CISO is not about doing less security. It is about doing the right security with the resources you have. The steps above give you a practical path from "we have AI in production and no security process" to "we have visibility, testing, monitoring, and a response plan."

The biggest mistake companies make is waiting for a security incident to force action. The second biggest mistake is assuming their cloud provider or their traditional security tools have AI covered. Neither is true.

Start with the inventory. Run the basic tests. Set up monitoring. Write the policy. Get external validation. You can do all of this with your existing team, and the cost of doing it now is a fraction of the cost of dealing with an AI security incident later.

If you want to accelerate this process, BeyondScale works with companies at exactly this stage. Our services are designed for technical teams that need AI security expertise without the overhead of building it in-house. Reach out and we will scope an assessment based on your specific AI systems and risk profile.