Giuliano Liguori

Kenovy

Kenovy is a digital consulting firm, committed to developing innovative solutions that meet the relentless need for ever better digital services.

Chief Executive Officer and Co-Founder

Digital Leaders

The Digital Transformation HUB - Digital Leaders is a global initiative promoting Digital Transformation.

Founder and Publisher

Rotary International

Presidente Commissione Artificial Intelligence 
Anno Rotariano 2024 - 25

Rotary Club Afragola - Frattamaggiore "Porte di Napoli"
District 2101

Member

CIO Club Italia

Co-Founder

SwissCognitive, World-Leading AI Network

 Global AI Ambassador 2022

CTP SpA

Chief Innovation Officer

Chief Information Officer / IT Manager

IT Service Manager

IT Operations Manager

IT Support Engineer

BE Informatica

Chief Digital Officer (CDO)

SOFTWARE DESIGN SpA

IT Support Specialist

Business Intelligence Specialist

Chief Executive Officer and Co-Founder Kenovy | Vice President CIO Club Italia

At Kenovy, our team is spearheading digital consulting initiatives, focusing on innovative solutions for a demanding digital service landscape. Leveraging expertise in digital transformation and IT management has positioned us to drive business growth and operational excellence. We've embraced the challenge of continuously meeting the dynamic needs of our clients, ensuring they stay ahead in an ever-evolving digital economy.

My role as CEO encompasses not just leading our organization but also sharing knowledge on platforms like Digital Leaders. Contributing to a global dialogue on digital trends, from AI to cybersecurity, reflects my commitment to influencing and guiding digital strategy. It's through this dedication that we've enabled our clients to navigate change effectively, turning disruption into opportunity for sustainable success.

🧠 The modern AI stack is no longer optional knowledge — it’s a leadership skill.

This infographic nails the 5 pillars of modern AI that every organization (and executive team) should understand to compete in 2025:

1. Generative AI ✨
The engine of creation — text, image, video, code. Powered by LLMs, diffusion models, RLHF, fine-tuning, and prompt engineering.


2. Large Language Models (LLMs) 🧩
The core intelligence layer — transformers, context windows, embeddings, scaling, instruction tuning, evaluation, and inference optimization.


3. Retrieval-Augmented Generation (RAG) 🔎
The grounding layer — retrieval pipelines, vector DBs, chunking, re-ranking, knowledge graphs, caching, and source attribution for factual accuracy.


4. AI Agents 🤖
The autonomy layer — perception, reasoning, planning, memory, tool access, multi-step execution, orchestration, and continuous improvement.


5. Agentic AI 🚀
The systems layer — multi-agent collaboration, goal-oriented planning, self-correction, human-AI teaming, adaptive learning, and real-world tool integration.



The shift is clear:
We’re moving from AI that answers → to AI that executes outcomes.

👇 Which pillar do you think will create the biggest competitive advantage in the next 12–18 months?

#AI #GenerativeAI #LLMs #RAG #AIAgents #AgenticAI #DigitalTransformation #Innovation #FutureOfWork

Giuliano Liguori 

AI Agents are quickly becoming the operational layer of modern AI — not just “chatbots,” but systems that can plan, reason, act, learn, and adapt toward a goal.

This infographic captures the shift clearly:

An AI agent starts with goals and tasks, guided by a system prompt and (often) human oversight.

It senses the environment, then decides what to do next.

It relies on memory (short-term + long-term) to stay consistent and improve over time.

And it uses tools — web, code, apps, data, functions — to actually execute work in the real world.

What’s most important is not the academic debate around “agents vs agentic AI,” but the reality:
AI systems are moving from passive outputs to autonomous, multi-step behavior that delivers outcomes.

We’re entering the era where intelligent agents become teammates — orchestrating workflows, accelerating decisions, and scaling expertise across the enterprise.

How are you already using AI agents in your organization, and where do you see the biggest next opportunity?

#AI #AIAgents #AgenticAI #GenerativeAI #Automation #DigitalTransformation #FutureOfWork

🔐 Learning Cybersecurity with Python: Ethical Projects for Building Real Defensive Skills

Cybersecurity is one of the most strategic skill areas for modern organizations.
Python plays a key role in helping professionals understand system behavior, identify vulnerabilities and strengthen defenses.

Here are some ethical, hands-on project ideas that mirror real security workflows while promoting responsible learning:

Beginner Level
• Keylogging for defensive monitoring and endpoint behavior analysis
• Port scanning to understand network exposure and open service enumeration
• Traffic load simulation to study resilience and DDoS mitigation strategies

Intermediate Level
• Vulnerability scanning to identify misconfigurations and outdated components
• Packet sniffing for network forensics and intrusion detection
• Secure password generation and rotation tools

Advanced Level
• Wi-Fi auditing utilities for testing wireless security posture
• NFC security testing to validate contactless communication safety
• Email authentication and spoof-testing tools for strengthening SPF/DKIM/DMARC policies

Used responsibly, these projects help professionals deepen their understanding of attack surfaces — not to exploit them, but to anticipate threats, reinforce security controls and protect digital ecosystems.

Cybersecurity is ultimately about resilience, trust and proactive defense.

Credit to the author 

#Cybersecurity #EthicalHacking #Python #InfoSec #CyberDefense #SecurityEngineering #BlueTeam #RedTeam #AIandSecurity

🧩 AgentOps is becoming the new MLOps — but for autonomous AI.

As AI agents move from demos to real production workflows, the differentiator is no longer the model alone.
It’s the operational stack behind the agents.

This infographic captures the full AgentOps (Agent Operations) Stack end-to-end:

🔹 Planning & Tasks
Goal definition, task decomposition, role assignment, multi-agent coordination, dynamic reprioritization.

🔹 Memory & Context
Short/long-term memory, episodic recall, vector DBs, semantic indexing, temporal awareness.

🔹 Execution
Tool invocation, API/function calling, code generation, document/data processing, multi-step reasoning.

🔹 Monitoring
Real-time tracking, logging & metrics, health checks, error detection, auditing dashboards.

🔹 Optimization
Feedback loops, RL, self-adaptation, reward modeling, iterative improvement.

🔹 Governance
Privacy, compliance, risk assessment, bias detection, guardrails, human-in-the-loop validation.

🔹 Infrastructure
Compute, model serving, gateways, caching, protocols, observability, containerization.

Bottom line:
Agents don’t scale without operations.
AgentOps is what turns “smart” into reliable, secure, cost-controlled, outcome-driven AI.

👇 Which layer do you think is the biggest blocker for enterprise adoption right now?

#AgentOps #AIAgents #AgenticAI #LLMs #MLOps #RAG #Automation #AIInfrastructure #DigitalTransformation

How I explain AI to business teams in 30 seconds 👇

This infographic is a great map of the landscape, because it shows AI in three clear layers:

🟩 Traditional AI
Predicts, classifies, and detects anomalies.
Think: demand forecasting, fraud detection, ticket routing, risk models.

🟦 Generative AI
Creates and automates knowledge work.
Think: content generation, workflow automation, copilots, and RAG systems that answer using your company data.

🟪 Agentic AI
Does the work end-to-end.
Think: AI agents that call tools/APIs, coordinate with other agents, and integrate directly into products and services.

The real shift for 2025:
We’re moving from AI that predicts → to AI that creates → to AI that executes outcomes.

👇 Where is your organization today on this ladder — Traditional, Generative, or Agentic?

#AI #TraditionalAI #GenerativeAI #AgenticAI #AIAgents #RAG #DigitalTransformation #Innovation #FutureOfWork

🚀 The Complete Generative AI Tech Stack: 6 Layers Powering Autonomous AI Agents

The rapid evolution of Generative AI has brought us to a new frontier: autonomous AI agents capable of planning, reasoning and executing tasks across complex ecosystems.
This infographic by @goyalshalini brilliantly illustrates the six foundational layers enabling this transformation.

🔹 1. Infrastructure Layer (IaaS / Compute & Storage)

The backbone of every AI system — including GPUs/TPUs, cloud providers, vector databases, containerization and orchestration frameworks like Kubernetes.

🔹 2. Foundation Model Layer (LLM Core)

Pretrained and open-source LLMs (GPT, Claude, LLaMA, DeepSeek, Mistral, Phi) combined with tokenization, fine-tuning techniques, prompt engineering and scalable deployment architectures.

🔹 3. Retrieval-Augmented Generation (RAG Layer)

Embedding models, chunking, vector search, hybrid ranking and context injection bring factual grounding, accuracy and real-time knowledge into LLM workflows.

🔹 4. Generative AI Layer (Applications & UX)

Text, image, audio and code generation interfaces — from chatbots to multimodal systems — enabling user-facing experiences powered by advanced AI capabilities.

🔹 5. AI Agent Layer (Autonomous Behaviors)

Planning, task chaining, API execution, memory systems, reasoning patterns (ReAct, CoT, ToT) and multi-agent collaboration frameworks such as LangChain, CrewAI and AutoGen.

🔹 6. Agentic AI Layer (Coordination & Orchestration)

Protocols, communication frameworks, environment simulation, governance, safety mechanisms and adaptive intelligence that allow multiple agents to coordinate effectively.

This layered architecture explains why AI is rapidly moving from isolated tasks to complex, end-to-end autonomous workflows.
Understanding these layers helps organizations design scalable, robust and safe AI systems ready for real-world deployment.

#GenerativeAI #AIagents #AgenticAI #LLMs #RAG #ArtificialIntelligence #Innovation #DigitalTransformation #AIArchitecture

Chief Executive Officer @ Kenovy

🧠 How AI really works — in 8 simple steps.

Too often AI feels like a black box. This infographic breaks it into a clear, business-friendly workflow that every leader and practitioner should know:

1️⃣ Problem definition – What are we solving and how do we measure success?
2️⃣ Data collection & preparation – Clean, label, split, and make data usable.
3️⃣ Model selection & algorithm design – Choose the right approach for the goal.
4️⃣ Model training – Learn patterns from data, reduce errors, iterate.
5️⃣ Evaluation – Test on unseen data and validate KPIs.
6️⃣ Fine-tuning & optimization – Improve accuracy, robustness, and efficiency.
7️⃣ Deployment – Put the model into real workflows and monitor performance.
8️⃣ Ethical considerations – Ensure fairness, transparency, privacy, and safety.

AI success is not about one step.
It’s about building a repeatable, responsible loop that turns data into impact.

👇 Which step do you see organizations struggle with the most today?

#AI #ArtificialIntelligence #MachineLearning #DataScience #DigitalTransformation #Innovation #ResponsibleAI

🧠 The evolution of AI agents is happening in layers — and we’re moving up the stack fast.

This infographic breaks AI agents into 5 levels of capability, from simple automation to truly autonomous digital personas:

1️⃣ Level 1 — Rule-Based Agents
If-then workflows. Useful, but static. Think basic automation.

2️⃣ Level 2 — Tool-Using Agents
Agents that call APIs/tools and learn from interaction. Great assistants, still narrow.

3️⃣ Level 3 — Strategic AI Agents
Multi-step planning, reasoning, memory, and collaboration. This is where real task chains emerge.

4️⃣ Level 4 — Context-Aware Autonomous Agents
Long-term memory + proactive behavior. They operate independently and anticipate needs.

5️⃣ Level 5 — Superintelligent Digital Personas
Theoretical AGI territory. Powerful, but not yet achievable — and full of open questions.

The big takeaway:
We’re shifting from “AI that responds” → to AI that plans, executes, and improves continuously.

👇 Where do you think most companies are today — Level 2 or Level 3? And what’s needed to reach Level 4?

#AI #AIAgents #AgenticAI #Automation #GenerativeAI # DigitalTransformation #FutureOfWork

📌 Machine Learning algorithms aren’t the hard part.
Building the right process around them is what makes ML work in the real world.

This infographic lays out a simple, repeatable roadmap:

1️⃣ Understand the data
Clean it, select the right features, split it properly. Garbage in = garbage out.

2️⃣ Choose the right algorithm
Regression, decision trees, clustering, etc. The “best” algorithm depends on the problem, not the hype.

3️⃣ Train the model
Feed training data, tune parameters, track learning.

4️⃣ Test the model
Validate on unseen data, measure accuracy, check for overfitting.

5️⃣ Optimize the model
Hyperparameter tuning, cross-validation, feature engineering. This is where performance is won.

6️⃣ Deploy the model
Real-time predictions, monitoring in production, retraining as data changes.

The takeaway: ML success is a loop — not a one-time build.
The teams that win are the ones who operationalize it end-to-end.

👇 Which step do you see organizations underestimate the most?

#MachineLearning #AI #DataScience #MLWorkflow #Analytics #DigitalTransformation #Innovation

📊 Important data terms — explained simply.

Data is at the core of every modern business conversation, yet many teams still mix concepts that mean very different things. This infographic is a useful refresher on the building blocks of the data world:

🔍 Data Mining — discovering patterns in datasets
📈 Data Analysis — interpreting data to generate insights
📊 Data Visualization — communicating insights through charts/graphs
🧠 Data Science — extracting knowledge using statistics + ML

🏗️ Data Modeling — structuring data for systems and analytics
🔗 Data Integration — combining data across sources
🧹 Data Cleaning — removing errors and inconsistencies
🏢 Data Warehouse — centralized, structured analytics repository

🧩 Data Mart — smaller, department-focused warehouse slice
🌊 Data Lake — raw + processed data at scale
🪵 Delta Lake — reliability layer on top of data lakes
🚚 Data Pipeline — moving & transforming data between systems

🕸️ Data Mesh — decentralized, domain-owned data architecture
🏞️ Data Lakehouse — lake + warehouse combined
🐊 Data Swamp — unstructured lake with poor governance
🧵 Data Fabric — unified integration layer for end-to-end insight

Understanding these terms isn’t academic — it directly shapes how you design your data strategy, governance, and AI readiness.

👇 Which of these concepts is still most misunderstood in your organization?

#Data #DataScience #Analytics #DataEngineering #AI #DigitalTransformation #BusinessIntelligence

Just shared this brilliant mind map on the 15 key architectural characteristics of AI agents — absolutely packed with insights!

Modularity, evolvability, context awareness, security compliance… everything you need to design robust agents.

Huge thanks to @Python_Dv for creating this gem

☁️ Cloudflare vs AWS vs Azure — same cloud game, very different strengths.

This infographic (credit: ByteByteGo) is a clean way to see how the big three map across modern needs — especially now that edge compute + AI agents + RAG workloads are becoming mainstream.

Here’s the strategic takeaway:

🔸 Cloudflare = edge-first cloud
Best when latency, global distribution, and “compute at the perimeter” matter.
Workers, R2, D1, Vectorize, Workflows, and Agents are designed to run close to users — with simpler primitives and fast iteration.

🔸 AWS = deepest enterprise cloud stack
The broadest set of services and the most mature ecosystem.
Lambda, S3, RDS, ECS/Fargate, Step Functions, Bedrock, SageMaker — ideal for complex architectures at massive scale.

🔸 Azure = Microsoft-native enterprise + OpenAI ecosystem
Powerful for organizations standardizing on Microsoft.
Azure Functions, Blob, Cosmos DB, AKS, Logic Apps, Cognitive Search, and especially Azure OpenAI / AI Agent Service shine in corporate environments.

What this means in practice:
✅ Edge-native apps / global low-latency → Cloudflare
✅ Maximum flexibility + service depth → AWS
✅ Microsoft stack + OpenAI integration → Azure

We’re not choosing “a cloud” anymore.
We’re choosing the right execution layer for the workload — especially for AI-driven systems.

👇 If you were building an AI agent platform today, where would you start — and why?

Credit: ByteByteGo
#Cloudflare #AWS #Azure #CloudComputing #EdgeComputing #AIAgents #AgenticAI #RAG #DevOps #DigitalTransformation

🔵 Agentic AI is not a single tool — it’s a full-stack architecture.

This infographic (credit: Analytics Vidhya) explains the layered reality behind autonomous AI:

🧠 LLMs are the core (tokenization, prompts, inference, APIs).
🤖 AI Agents add planning, reasoning, memory, and tool use.
🧩 Agentic Systems (multi-agent) enable collaboration, routing, RAG, and state coordination.
🏗️ Agentic Infrastructure makes it enterprise-ready: observability, security, HITL controls, retries, and cost governance.

In short:
LLMs = intelligence
Agents = autonomy
Multi-agents = scale
Infrastructure = reliability

The companies winning in 2025 won’t just “use an LLM.”
They’ll build agentic systems that execute real work end-to-end.

👇 Where do you see the biggest near-term impact of agentic AI in your industry?

#AgenticAI #AIAgents #LLMs #GenerativeAI #RAG #Automation #AIArchitecture #DigitalTransformation

🧠 17 Essential Prompt Engineering Techniques for Getting Better Results with ChatGPT

As generative AI becomes a core component of professional workflows, the ability to craft effective prompts is now a strategic skill.
This infographic highlights 17 powerful prompt engineering techniques that help unlock clearer, more accurate and more actionable responses.

Some of the most impactful approaches include:

🔹 1. Specific Prompts – Clearly define what you want (“Write an accurate one-line definition of photosynthesis”).
🔹 2. Give a Role – Assign expertise to the model (“You are an expert career coach”).
🔹 3. Provide Context – Explain the situation to improve relevance.
🔹 4. State the Task Explicitly – Make the request clear and actionable.
🔹 5. Specify Output Format – Tables, bullet points, summaries, frameworks, etc.
🔹 6. Specify Tone – Persuasive, formal, conversational, analytical.
🔹 7. Set a Style/Genre – Twitter thread, strategy memo, report, press release.
🔹 8. Include Data or Files – Guide the model using attachments or statistics.
🔹 9. Image Generation Prompts – Request minimal visuals or conceptual sketches.
🔹 10. Hypothetical Scenarios – Explore possibilities and risks.
🔹 11. Progressive Prompting – Build responses step by step.
🔹 12. Step-by-Step Frameworks – Ask for structured processes or playbooks.
🔹 13. Sequential Requests with Sources – Summaries with citations and chronology.
🔹 14. Contrasting Perspectives – Expand analysis with pros/cons or opposing views.
🔹 15. Conditional Prompts – “If X happens, estimate Y impacts…”
🔹 16. Multi-Role Dialogues – Role-play scenarios for deeper reasoning.
🔹 17. Future Forecasting – Analyze long-term trends and outlooks.

Mastering these techniques dramatically improves the quality of AI-assisted writing, strategy, research, analysis and problem-solving.

Credit to Gina Acosta (@ginacostag_)

#PromptEngineering #GenerativeAI #ChatGPT #AIProductivity #DigitalTransformation #AIEducation #FutureOfWork

🔐 Cybersecurity isn’t just tech — it’s documentation, discipline, and repeatable execution.

If you want security to scale, you need the right templates, logs, and operating documents in place. This infographic highlights the core artifacts every serious security program should maintain across:

🛡️ Information Security
• Data classification register
• Incident reporting & tracking
• Encryption key management
• Access rights/permissions matrix
• Data breach notification log
• Document retention tracker

🌐 Network Security
• DDoS mitigation plan
• NAC / VPN usage logs
• Event correlation tracker
• IP whitelist/blacklist tracker
• Patch management schedule
• Network device inventory

☁️ Cloud Security
• Cloud security configuration
• Incident response log
• Backup & recovery testing
• Asset inventory tracker
• Access control matrix
• Cloud security dashboard

🧩 Application Security
• Secure coding checklist
• Static code analysis log
• Misconfiguration tracker
• Threat modeling templates
• Risk assessment matrix
• Secure mobile app testing tracker

📌 Security Management
• Backup & recovery plan
• Compliance management
• Password policy
• Acceptable use policy
• Info transfer / classification / disposal policies

The fastest way to mature security is not buying more tools.
It’s building operational muscle that makes security measurable and auditable.

👇 Which of these documents is missing in most organizations you see?

#Cybersecurity #InfoSec #CloudSecurity #AppSec #RiskManagement #Governance #Compliance #SecurityOperations

📈 The AI tool race is officially mainstream — and the numbers are staggering.

According to AITools.xyz, ChatGPT alone is drawing 4.7B monthly visits (Jan 2025) 🤯
But what’s even more interesting is what comes next:

🎨 Canva — 887M
🌍 Google Translate — 595M
🧠 DeepSeek — 268M (explosive growth after open-source reasoning release)
💬 Character.AI — 226M
🔎 Perplexity — 133M
✨ Gemini — 118M
🤖 Claude — 105M
…and a long tail of specialized tools reshaping search, creativity, education, writing, and productivity.

This isn’t just “AI adoption.”
It’s a clear signal that AI has become a daily operating layer for work and life — and the ecosystem is fragmenting into purpose-built leaders.

The question now:
Which category will dominate next — chat, search, design, coding, or autonomous agents?

👇 What AI tool do you personally use the most, and why?

Credit / Source: AITools.xyz
#AI #GenerativeAI #AITools #ChatGPT #Perplexity #Gemini #Claude #DeepSeek #DigitalTransformation #Innovation

🤖 Excited to see Bitrix24 launch the new MCP Hub!

Honoured to be a trusted Bitrix24 Gold Partner in Europe and to bring this innovation to our clients 🪄✨
Kenovy #Bitrix24

The 𝗛𝘂𝗮𝘄𝗲𝗶 𝟮𝟬𝟮𝟱 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 & 𝗜𝗻𝘁𝗲𝗹𝗹𝗲𝗰𝘁𝘂𝗮𝗹 𝗣𝗿𝗼𝗽𝗲𝗿𝘁𝘆 𝗙𝗼𝗿𝘂𝗺 once again underlined how openness in IP and sustained R&D investment can unlock the next wave of digital innovation.

Among the Top Ten Inventions presented this year is the 𝗦𝗰𝗮𝗹𝗲-𝗨𝗽 𝗨𝗹𝘁𝗿𝗮-𝗟𝗮𝗿𝗴𝗲-𝗦𝗰𝗮𝗹𝗲 𝗦𝘂𝗽𝗲𝗿𝗣𝗼𝗗 𝗦𝗲𝗿𝗶𝗲𝘀. One of today’s main bottlenecks for large-scale AI infrastructure is interconnect technology – how to link massive numbers of chips and SuperPoDs over long distances while keeping connections reliable, high-speed and low-latency.

Leveraging decades of expertise in connectivity and a series of system-level innovations, Huawei has introduced the 𝗔𝘁𝗹𝗮𝘀 𝟵𝟱𝟬 𝗦𝘂𝗽𝗲𝗿𝗣𝗼𝗗 (with 8,192 Ascend NPUs) and the 𝗔𝘁𝗹𝗮𝘀 𝟵𝟲𝟬 𝗦𝘂𝗽𝗲𝗿𝗣𝗼𝗗 (with 15,488 Ascend NPUs). These systems are designed to deliver industry-leading performance in computing power, memory capacity and interconnect bandwidth.

Building on this, Huawei has also announced the 𝗔𝘁𝗹𝗮𝘀 𝟵𝟱𝟬 𝗦𝘂𝗽𝗲𝗿𝗖𝗹𝘂𝘀𝘁𝗲𝗿 (over 500,000 Ascend NPUs) and 𝗔𝘁𝗹𝗮𝘀 𝟵𝟲𝟬 𝗦𝘂𝗽𝗲𝗿𝗖𝗹𝘂𝘀𝘁𝗲𝗿 (over one million Ascend NPUs) – large-scale computing clusters composed of multiple SuperPoDs. Together, they are positioned to provide the abundant computing power needed for the rapid advancement of AI in the years ahead.

This is a strong example of how Huawei uses intellectual property, open collaboration and engineering excellence to drive progress in AI computing.

Huawei #Huawei #IPR2025 #IPMatters

𝗗𝘆𝗻𝗮𝗺𝗶𝗰 𝗣𝗿𝗼𝗳𝗶𝗹𝗲𝘀 𝗮𝗻𝗱 𝗣𝗲𝗿𝘀𝗼𝗻𝗮𝗹𝗶𝘇𝗲𝗱 𝗟𝗮𝘆𝗼𝘂𝘁𝘀 𝗶𝗻 𝗕𝗶𝘁𝗿𝗶𝘅𝟮𝟰: 𝗚𝗶𝘃𝗲 𝗬𝗼𝘂𝗿 𝗧𝗲𝗮𝗺 𝗮 𝗙𝗮𝗰𝗲
In today’s digital organizations, every user is more than just a name on a list — they’re a key part of the company’s structure.
 
With Bitrix24, each profile becomes an interactive company card, helping you highlight people, roles, and skills across your organization.
Customizing employee profiles enhances 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, 𝗶𝗻𝘁𝗲𝗿𝗻𝗮𝗹 𝗰𝗹𝗮𝗿𝗶𝘁𝘆, 𝗮𝗻𝗱 𝘁𝗲𝗮𝗺 𝗲𝗻𝗴𝗮𝗴𝗲𝗺𝗲𝗻𝘁.

 Especially in large or distributed teams, having well-structured and consistent profiles helps streamline communication and strengthen your corporate culture.

Kenovy Bitrix24 #Kenovy #Bitrix24

Great quick reference from @SecurityTrybe! 

🔥 8 common cyberattacks every professional should know: 
1. Phishing 
2. Malware 
3. DoS/DDoS 
4. Man-in-the-Middle 
5. SQL Injection 
6. Zero-Day Exploit 
7. Advanced Persistent Threat (APT) 
8. Ransomware 

Clean infographic, perfect for awareness training or sharing with your team. 
Thanks Security Trybe for the solid breakdown! 🛡️ 

#CyberSecurity #InfoSec

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