~/docs/resources/weekly-ai-security-drill-from-awesome-ai-security.mdLast modified: Just now

AUDIENCE:SOC teams, AppSec, AI engineers, security leads, and GRC teams who need a practical way to test and harden LLM and agent features.

PROMISE:At the end of each weekly session, you will have one governance decision, one red-team result, one defensive change to trial, and a small evidence bundle to keep.

Turn the “Awesome AI Security” Repo Into a Weekly Drill

A repeatable method to move from bookmarking tools to running tests, adding controls, and keeping evidence.

One repo. One weekly routine. Clear outputs your team can review.

AI security resources are scattered. It is easy to lose time across tabs, tools, and notes.

A large curated repo can help. But only if you turn it into a routine, not a bookmark.

This resource gives you one mechanism. A weekly drill that touches governance, testing, defenses, and monitoring.

You finish each session with outputs you can reuse. Even if you start small, you build a trail you can show in reviews and audits.

##Pre-flight checklist (before you run any AI security tool)

-Pick one target. Name the exact LLM endpoint, agent workflow, or feature you will test.
-Confirm data boundaries. Which inputs might contain secrets, customer data, or internal code?
-Use a lab or isolated environment when possible. Avoid surprises in production.
-Define stop rules. Decide which findings force you to pause rollout (leakage, unsafe actions, auth bypass).
-Decide how you will record results. A folder, ticket, or wiki page is enough.
-List the owners. Who can change prompts, policies, middleware, and logging?
-Write one sentence on why this target matters. It keeps the drill focused.

##The weekly drill (one pass, end to end)

[01]Choose one risk theme (leakage, prompt injection, unsafe actions, monitoring gaps)

[02]Adopt one requirement and write it as a testable rule

[03]Run one red-team test in a lab and save the raw results

[04]Triage findings and pick the one you will fix or reduce next

[05]Pilot one defensive control and re-test the same prompts

[06]Add one logging or monitoring improvement and confirm the signal exists

[07]Save an evidence bundle and open one follow-up ticket

##Tool-to-task mapping (pick one item from each bucket)

Group 1 : Red-team and probing (find weak behaviors)

->garak: modular probes for prompt injection, jailbreaks, and data leakage (Source: Otto Sulin/Awesome-AI-Security)
->promptfoo: structured red teaming with CI/CD-friendly workflows (Source: Otto Sulin/Awesome-AI-Security)
->PyRIT: generate adversarial prompts and evaluate responses against safety benchmarks (Source: Otto Sulin/Awesome-AI-Security)
->BlackIce: containerized red-teaming lab kit for reproducible testing (Source: Otto Sulin/Awesome-AI-Security)

Group 2 : Runtime policy enforcement (block or transform risky inputs/outputs)

->Guardrails: enforce runtime policies with validators; can redact, rewrite, or retry (Source: Otto Sulin/Awesome-AI-Security)
->Start with two policies: PII handling and unsafe instruction handling
->Log every block or transform decision with a reason code
->Re-test the same prompts after changes to confirm the control triggers

Group 3 : Monitoring and observability (see drift and new attack patterns)

->LangKit: track signals like prompt injection similarity and PII patterns; compatible with whylogs (Source: TalEliyahu/Awesome-AI-Security)
->Pick a weekly dashboard view: top triggers, top risky routes, and top failing tests
->Decide what you will alert on vs. what you only trend
->Keep sample payloads (sanitized) for repeatable regression tests

Group 4 : Agents and tool-use (reduce unsafe or unintended actions)

->If you use agents, map every external tool or function the model can call
->Review how your system validates tool calls and their parameters
->Treat context and tool permissions as security boundaries
->Look for MCP security resources if you use Model Context Protocol (Source: Otto Sulin/Awesome-AI-Security)

##Operational flow (from test to control to evidence)

Use this to keep the drill small and complete. It helps you go from a finding to a change you can prove.

1) Scope and safety

├─Define the target and allowed test window
├─Choose a risk theme and success criteria
└─Prepare a lab or safe test harness

2) Execute tests

├─Run one probing tool and save the raw output
├─Reproduce the top finding with a minimal prompt set
└─Tag the finding: injection, leakage, unsafe action, or other

3) Apply one control

├─Implement one concrete rule (input validation or output filtering is a common start)
├─Add or tighten a runtime policy layer if you have one
└─Re-test the same prompts and compare behavior

4) Observe and document

├─Add one log field you will rely on during incidents
├─Create one dashboard or saved search for the new signal
└─Store an evidence bundle and open one follow-up ticket

##FAQ (practical decisions people get stuck on)

Should I run these tools against production?

Prefer a lab or isolated environment first. If you must test production, use a controlled prompt set, clear stop rules, and an agreed window. Treat tests like any other potentially disruptive security activity.

What evidence should I keep from each weekly drill?

Keep outputs you can replay and review later. Save the exact prompt set, raw tool output, enforcement logs (if you use runtime policies), and one short note on what you changed and why.

How do I avoid “we tested, but nothing changed”?

Force one small change each session. It can be one validation rule, one new reason code in logs, or one monitoring improvement. Then re-test the same prompts to confirm the change matters.

How do I link tests to governance without writing vague policy?

Write the requirement as a testable rule. Define what you reject, what you redact, and what you log. Then link the rule to a tool run and to the evidence you stored.

What if I do not have SIEM integration yet?

Start with local logs you can export. Keep a consistent schema like timestamp, target, prompt ID, policy outcome, and finding tag. Later you can forward the same fields into your SIEM.

What is the most common mistake with a big curated repo?

Collecting links instead of running a routine. One item from each bucket beats ten bookmarks.

##Primary references (use them, but keep the drill as the main habit)

-Awesome-AI-Security (curated tools and frameworks): https://github.com/OttoSulin/Awesome-AI-Security
-OWASP Top 10 for LLMs (requirements you can turn into testable rules): https://genai.owasp.org/llm-top-10
-TalEliyahu/Awesome-AI-Security (observability and datasets index): https://github.com/TalEliyahu/Awesome-AI-Security