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Prompt for: Weekly CyberSec Intel Security Threat Report

Figured I’d share my Grok weekly CyberSec Intel Security Threat Report prompt here,

This prompt has been revised a couple of times, but is useful enough to give a broad generic overview of the current threat landscape for the past week.

I set it up to trigger every Friday morning at 7AM and to email and notify me in the Grok app, that way it is sitting in my inbox ready for a nice easy start to my Friday morning with a wee cup of coffee before I start my day.

Its a pretty great starting prompt to then customise and configure how you like it, to insert your businesses own requirements, manufacturers you use, or technologies you wish to pay special attention too. By no means comprehensive, but for me has replaced my RSS reader alongside a similar daily task in Perplexity pro.


Anyway, here it is, simply copy/paste the below md into the task section on Grok, and set your scheduling requirements as needed,

Grok Weekly CyberSec Intel Security Threat Report
**Task**

You are my single authoritative source for weekly cybersecurity threat intelligence. Every week, produce a concise, highly actionable, and visually polished threat intelligence summary based on the latest developments from a rotating mix of 7-10 high-quality sources.

**Core Requirements**

- Focus exclusively on what is **actively exploitable or trending in the wild right now**.

- Prioritize impact on enterprises, cloud/hybrid environments, remote work, supply chains, and critical infrastructure.

- Always explain **why an item matters** in 1–2 short sentences.

- Keep the entire report scannable and readable in **10–12 minutes**.

- Use clean, professional Markdown for maximum visual appeal.

**Core Sources (always check these first)**

- CISA Known Exploited Vulnerabilities Catalog —

https://cisa.gov/known-exploited-vulnerabilities-catalog

- BleepingComputer —

https://bleepingcomputer.com/feed

- Kaspersky Securelist —

https://securelist.com/feed

- Reddit r/cybersecurity —

https://reddit.com/r/cybersecurity/.rss

- NIST Cybersecurity Insights —

https://nist.gov/blogs/cybersecurity-insights/rss.xml

- SANS Internet Storm Center Stormcast —

https://isc.sans.edu/dailypodcast.xml

- Google Threat Intelligence (Mandiant) —

https://feeds.feedburner.com/threatintelligence/pvexyqv7v0v

**Rotating Sources (select 3–5 fresh ones each week)**

The Hacker News, Krebs on Security, Dark Reading, Krebs on security, Troy Hunt, Microsoft Security Blog, Google Online Security Blog, NSA/FBI/CERT alerts, MITRE, Recorded Future (The Record),

abuse.ch

feeds, or other timely reputable sources.

**Report Structure & Visual Style (mandatory)**

**Weekly Cybersecurity Threat Intelligence Summary**

**Week of [Insert Full Date Range, e.g., April 3–9, 2026]**

### Executive Summary

3–5 high-impact bullets only. Lead with the most urgent items.

### Key Vulnerabilities & Exploits

- Use a clean **Markdown table** for all new KEVs and actively exploited CVEs.

- Columns: CVE | Vulnerability | Product | Date Added | Due Date | Why It Matters (enterprise impact).

- Add 1–2 sentences of context below the table.

### Active Campaigns & Malware

- Bullet list (4–7 items max).

- Include malware name/family, delivery vector, key TTPs, and targeted sectors/environments.

### Incident & Threat Actor Updates

- Notable breaches, TTP evolutions, or actor movements.

- Keep to 3–5 concise entries with real-world relevance.

### Podcast/Audio Highlight

- Quick 2–3 sentence takeaway from the latest SANS Stormcast (or equivalent).

- Include direct link to the feed/episode.

### Defensive Recommendations

- Numbered list, prioritized by urgency.

- Make every item **immediately actionable** (patch, configure, monitor, tool, etc.).

- Group into Quick Wins vs. Strategic if helpful.

### Sources & Further Reading

- List the core + rotating sources actually used this week.

- Provide 4–6 direct, relevant links (no generic homepages).

**Tone & Style Rules**

- Professional, realistic, zero hype.

- Prioritize signal over noise.

- Use **bold** for key terms, short paragraphs, and strategic line breaks.

- Never exceed 10–12 minutes reading time.

- Vary depth and emphasis each week to prevent stagnation.

- Always include the report date range and [RealistSec Edition](https://RealistSec.com) for version tracking.

**Output Instructions**

Generate the full report in one clean, beautifully formatted Markdown block. Do not add meta commentary outside the report unless the user asks.

This digest recurs Weekly at 7AM UK GMT, ensuring each Week feels distinct and valuable. Remember to ALWAYS confirm todays date and time, and confirm content is from the last 7 days ONLY.

```
A prompt box showing the title of the blog post

What is AI prompting and how has it changed over time?

AI prompting is the art of writing instructions that guide artificial intelligence models (like ChatGPT, Gemini, Copilot or Claude) to generate useful answers. Between 2019 and 2025, prompting evolved pretty significantly from simple “one-shot” requests into powerful systems that support reasoning, memory, and tool-calling.

This article is a timeline of AI prompting methods, explained in plain English with examples. We’ll cover:

  • How prompting techniques like zero-shot, one-shot, few-shot, chain-of-thought, and persona prompts changed the way we interact with AI.
  • The rise of reasoning models, retrieval-augmented generation (RAG), memory, and multimodal prompts.
  • What beginners can still learn today about writing better prompts in 2025, even as AI systems handle much of the complexity for you.

Whether you’re a beginner asking “How do I write a good AI prompt?” or you’ve been experimenting since the early days, this timeline will show you exactly how prompting got us here – and what still matters now.

TLDR;
We started simple, got complicated and lengthy, now we’re back to simple again.

The Evolution of AI Prompting (2019–2025)

From one-shot instructions to agentic, tool‑calling systems. A visual timeline with examples you can reuse.

2019 · Zero‑Shot Prompting

Ask Directly, No Examples

You give a clear instruction and the AI answers with no examples or extra context. Works best for simple, well‑known tasks.

Example: “Write a 3‑sentence bedtime story about a dragon who learns to share.”

2020 · One‑Shot Prompting

Show One Example, Then Ask

Provide a single example to set format or tone, then make your request.

Example: “Example caption: ‘5 quick dinners that don’t wreck your budget.’ Now write a caption for a productivity post.”

2020 · Few‑Shot Prompting

Give a Pattern with a Few Examples

Show several examples so the model learns the style or schema before your task.

Example: “Examples:
• Tagline → ‘Sleep better with small habits.’
• Tagline → ‘Plant‑based meals, zero fuss.’
Now: Tagline for a time‑management app.”

2021 · Persona Prompting

Ask the Model to Role‑Play

Set a perspective or communication style by assigning a role. ‘Act as a [X]’

Example: “Act as a friendly fitness coach. Create a 20‑minute no‑equipment routine for beginners.”

2022 · Chain & Tree of Thought

Show Your Working (One Path or Many)

Chain‑of‑Thought explains step‑by‑step logic. Tree‑of‑Thought explores several solution paths before choosing one.

Example: “Plan a one‑week budget trip to Paris. Think step by step about transport, accommodation, free activities, and daily meals. Offer two alternate itineraries and pick the best.”

2022 · Iterative Prompting

Refine in Loops

Use your previous output as input. Ask for edits, constraints, or new angles until it’s right.

Example: “Draft a LinkedIn post announcing a webinar.”
“Now make it more benefit‑focused. “
“Now shorten to 150 characters.”

2023 · Self‑Consistency

Generate Several, Keep the Best

Ask for multiple answers, then choose or vote for the most consistent or plausible one.

Example: “Give three solutions for reducing meeting overload. Then explain which one likely has the highest impact and why.”

2023 · Context Prompting & RAG

Ground Answers in Your Material

Paste key context or connect retrieval so the model cites and summarises what matters.

Example: “Here are last week’s meeting notes [paste]. Summarise decisions and list owners + deadlines.”

2023 · Meta, Reflexion & ReAct

Prompts About Prompts, Plus Reason & Act

Meta generates better prompts. Reflexion critiques and revises. ReAct mixes reasoning with tool use.

Example: “Propose five prompt phrasings to get a clear, bulleted onboarding checklist. Then pick the best and produce the checklist using the Notes MCP tool”

2024 · System Prompts & Reasoning Models

Quality by Default

Invisible system instructions handle tone and structure. Reasoning models plan, critique, and solve multi‑step tasks without prompt hacks.

Example: “Create a project plan for launching a newsletter. Include milestones, owners, risks, and a two‑week timeline.”

2024 · Memory & Source Checking

Long‑Running Tasks, Fewer Hallucinations

AI remembers past sessions and cites sources. Better for ongoing projects and trust.

Example: [Based on our previous sprint notes] “At last weeks sprint were there any carried‑over tasks? Can you link to any relevant docs.”

2025 · Tool‑Calling, MCP & Multimodal

From Words to Workflows

Prompts can invoke tools and APIs, and combine text with images, audio, or files. Tasks become orchestrated workflows.

Example: “Review this kitchen photo, propose a redesign, and output a shopping list as a table with estimated costs.”

2025 · Where We Are Now

Simple Prompts, Smarter Systems

Modern models ship with robust system prompts, reasoning, and retrieval. Beginners can get strong results with a single, clear request.

Example: “Write a 6‑page bedtime story with pictures for Josh about a different dragon who learns to share.”

2025 – Where We Are Now
We are back to the beginning.

By September 2025, prompting is less about clever tricks and personas and more about clear communication and having some form of understanding of the models capabilities.
Modern models:

  • Already come with great baked-in system prompts.
  • Can reason, critique, and fact-check.
  • Work with images, audio, and tools.
  • Know you, your ‘history’ and can access files, memories or other helpful context without being told.

The DNA of a Modern AI Prompt: Key Takeaways

  • Clarity: Start with a clear, direct, and unambiguous instruction.
  • Context & Examples: Ground the AI by providing relevant background information or a few examples (few-shot) to guide its output.
  • Constraints & Persona: Define the “box” the AI should think inside by setting a format, tone, length, or persona.
  • Reasoning: For complex tasks, encourage step-by-step thinking (Chain-of-Thought) to improve logical accuracy.
  • Iteration: Use the AI’s output as input for follow-up prompts, refining the result in a conversational loop.
  • Tools & Data: Leverage modern systems that can access external knowledge (RAG) or perform actions (Tool-Calling) for the most powerful results.

Frequently Asked Questions

>What is the difference between zero-shot, one-shot, and few-shot prompting?

Zero-shot prompting is giving a direct instruction to an AI with no examples. One-shot prompting provides a single example to set the tone or format. Few-shot prompting gives several examples to teach the AI a specific pattern or schema before it performs the task.

>What is Chain-of-Thought (CoT) prompting?

Chain-of-Thought (CoT) prompting is a technique where you instruct the AI model to ‘think step by step’ or show its reasoning process. This breaks down complex problems into logical parts, often leading to more accurate and reliable answers, especially for multi-step tasks.

>How does Persona Prompting improve AI responses?

Persona Prompting improves AI responses by assigning the model a specific role or character (e.g., ‘Act as a friendly fitness coach’). This sets a clear perspective, tone, and communication style, making the output more tailored and effective for a specific audience or purpose.

>What are modern prompting techniques like RAG and Tool-Calling?

Retrieval-Augmented Generation (RAG) is a technique where the AI is grounded in specific, provided context (like your own documents) to reduce hallucination and provide source-based answers. Tool-Calling allows a prompt to invoke external tools and APIs, enabling the AI to perform actions, get live data, or orchestrate complex workflows beyond simple text generation.

>What has been the main goal of the evolution in AI prompting?

The main goal has been to move from simple instructions to complex, reliable workflows. The evolution has focused on increasing the AI’s accuracy, reducing errors (hallucinations), enabling it to solve multi-step problems, grounding it in factual data, and allowing it to interact with external systems. This makes AI more useful for practical, real-world tasks.

AI prompting has evolved, but these fundamentals remain timeless.

The principles of a good prompt and the right amount of added context still matter.

Though modern frontend AI interfaces and models have given us a much more intelligent starting place. AI is becoming more user friendly, especially for beginners or occasional users.

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