Armando Javier Colón Aponte MSCJ, CBMA, COC, CASCC, CPPM,CFWAP, CWHBP, PCAP™’s Post

Focusing a lot of effort on Gen AI is a waste of time for 99% of companies. Cut your AI expectations in half. However here are 3 practical areas in tech/AI you SHOULD be running towards: 1. Clean your room before you go play! Your mum was right…... before you chase after “new toys”, make sure your company has good hygiene. There are tangible top and bottom line impacts by investing in Workflow and Collaboration tools (some with AI). They’re easy to integrate and bring transparency for multifunctional teams and your clients.   2. Does everyone know the score? It’s not sexy to invest in good data structure..... Most people don’t know what data scientists do..... and BI dashboards seem like magic boxes.  However, good, transparent data, with intelligent insights UNLOCKS your team’s abilities and allows less people to manage more. Ask yourself: Do you still brief from slides   or   do you brief from almost-real time dashboards? 3. Don’t let them rob the store! A great product with even tiny security flaws is useless when it gets hacked. I know firewalls, EDRs, encryption protocols, DoS attacks feels like IT claiming “the sky is falling.” (and needing more $$).  However, there are some really reliable, practical AI integrations with security now that keep IT headcount flat and make service faster while still providing reliable security at scale. You need to invest in security as you grow.... ***Note IT professionals: you need to be more transparent and use plain english when talking to other functional leaders. ss     The reality is there are some practical Ai integrations that work today, but I believe industries with “no-fail” requirements should first look at practical technology that can make an impact today's results…. After that you can go play with some shiny new toys.

Generative AI for imitation enterprise network" refers to using generative artificial intelligence techniques to create a simulated or "shadow" version of an existing enterprise network, allowing for testing, optimization, and analysis of network behavior without impacting the live production environment by essentially mimicking its structure and operations based on real data collected from the actual network. Key points about generative AI for imitation enterprise networks: Purpose: To replicate the complexities of a real enterprise network, including traffic patterns, device interactions, and potential failure scenarios, in a controlled environment for testing and troubleshooting new configurations, security measures, or network upgrades without disrupting live operations. How it works: Data collection: Gathering data from the live network, including network topology, device configurations, traffic flows, performance metrics, and event logs. Model training: Feeding this data into a generative AI model (like a Generative Adversarial Network (GAN)) to learn the underlying patterns and relationships within the network. Simulation generation: The trained model can then generate synthetic network data, creating a virtual replica of the real network that behaves similarly to the live environment. Potential benefits: Risk-free testing: Experiment with new configurations, security updates, or network changes in a simulated environment before applying them to the live network, minimizing potential disruptions. Capacity planning: Analyze network performance under different load scenarios to identify potential bottlenecks and optimize resource allocation. Incident response training: Create realistic network failure scenarios to practice troubleshooting and remediation strategies Network optimization: Identify areas for improvement by analyzing traffic patterns and performance metrics in the simulated environment Challenges: Data quality: The accuracy of the simulated network depends heavily on the quality and completeness of the data collected from the live network. Model complexity: Complex enterprise networks can require sophisticated generative models to accurately capture all relevant interactions and dependencies. Maintaining synchronization: Ensuring the simulated network stays aligned with changes occurring in the live environment Applications: Network upgrades and migration planning: Test new hardware, software, or network architectures before deploying them in the live environment. Security analysis: Simulate cyberattacks to evaluate the effectiveness of security measures and identify vulnerabilities Performance optimization: Analyze traffic patterns and identify areas for network optimization. DM for assistance with GeNAI for Cybersecurity. #cybersecurity #genai

Artificial intelligence (AI) has emerged as a transformative force, reshaping industries and revolutionising how we approach problem-solving. But have you ever wondered how this cutting-edge technology is being leveraged across various sectors? Prepare to embark on a captivating journey as we explore the sectoral utilisation of AI and its profound impact on the future of our industries. From finance to healthcare and manufacturing to cybersecurity, AI is making its mark, driving unprecedented efficiencies, enhancing decision-making, and unlocking new realms of possibility. But how exactly is this technology being harnessed, and what are the key benefits and challenges associated with its adoption? Join us as we delve into the heart of this AI revolution and uncover the insights that will shape the future of your industry. https://buff.ly/3Z8hi35 If you need help in identifying how to achieve this for your organisation, please do not hesitate to get in touch by email. [email protected] For more information, contact me at [email protected] or on +447767371001 #evangelize-consulting #digitaltransformation #linkedin #Innovation #leadership

Artificial intelligence (AI) has emerged as a transformative force, reshaping industries and revolutionising how we approach problem-solving. But have you ever wondered how this cutting-edge technology is being leveraged across various sectors? Prepare to embark on a captivating journey as we explore the sectoral utilisation of AI and its profound impact on the future of our industries. From finance to healthcare and manufacturing to cybersecurity, AI is making its mark, driving unprecedented efficiencies, enhancing decision-making, and unlocking new realms of possibility. But how exactly is this technology being harnessed, and what are the key benefits and challenges associated with its adoption? Join us as we delve into the heart of this AI revolution and uncover the insights that will shape the future of your industry. https://buff.ly/4fwPsma If you need help in identifying how to achieve this for your organisation, please do not hesitate to get in touch by email. [email protected] For more information, contact me at [email protected] or on +447767371001 #evangelize-consulting #digitaltransformation #linkedin #Innovation #leadership

it is very interesting to see the peaking of "generative AI", but wait there are several factors to have in line prior and after, prior includes - data, data quality, data granularity, data governance , data validation, and then add cybersecurity, a very robust and resilient defense mechanism with zero trust, the embeddings and vector databases for AI to execute seamlessly, the AI validation team and the prompt engineering team. Now you are ready to execute, and then there is more after for outcomes validation. Really think only technology can solve issues, never, we require "human in the loop" across several areas, and then the executive sponsorship and support. AI will make life easier if implemented correctly. The technology teams will have their stakeholders and teams in the spectrum, and they are the foundation pillars. They will need to have crucial discussions on the outcomes you want to fine-tune their magic to execute.

🚀 Generative AI: A game-changer for business, but are we prepared for the security challenges? As enterprises rush to adopt generative AI, we're seeing a revolution in content creation, data analysis, and decision-making. But with great power comes great responsibility - and significant security risks. Here's what you need to know: 🔑 Key concerns: 🔒 Data privacy: 63% of organizations limit data input into AI systems 🤖 Model safety: Risks of tampering and bias in AI models 🚧 Challenges: 📊 Lack of governance frameworks 👨💻 Shortage of AI security talent 🤝 Building trust and transparency 💡 Mitigation strategies: 🎛️ Establish data command centers 🛡️ Enhance data controls and classification 🔬 Implement regular AI model risk assessments 🔮 Future outlook: AI-powered threat detection Focus on model interpretability Industry-specific AI security standards Balancing innovation with security is crucial as the generative AI market grows at a CAGR of over 30%. Organizations must develop comprehensive governance frameworks, invest in continuous monitoring, and stay ahead of evolving threats. 🤔 Is your organization ready to navigate the security maze of generative AI? How are you addressing these challenges? Let's connect and discuss strategies for secure AI adoption in your enterprise!

Only 13% of organizations are ready for AI. This is a big problem. Cisco’s research shows the gap is growing. 93% predict AI will increase infrastructure workloads. 50% of IT budgets are allocating 10-30% to AI. Yet nearly 50% report AI investments haven’t delivered expected gains. Key barriers to AI readiness: - Critical talent shortages in AI expertise - Growing cybersecurity concerns around AI workloads - Persistent data silos limiting AI effectiveness - Complex governance requirements in the evolving regulatory landscape The message is clear: Organizations must act now on AI readiness or risk falling behind. According to the research, 59% of companies have only one year to implement their AI strategy before losing competitive advantage. To get ready, focus on these steps: 1. **Invest in Talent**: Hire or train employees in AI skills. This is crucial to bridge the talent gap. 2. **Enhance Cybersecurity**: Strengthen your cybersecurity measures to handle AI workloads safely. 3. **Break Down Data Silos**: Ensure data is accessible and integrated across the organization. This will improve AI effectiveness. 4. **Meet Governance Requirements**: Stay updated with regulatory changes and ensure compliance. Is your business ready for AI?

**Headline: Safeguarding AI Adoption with Security Services** Navigating the complexities of AI adoption is a critical challenge for industries today. As organizations eagerly embrace the transformative power of generative AI, security risks remain a significant concern. Optiv addresses this issue with its newly launched AI Security Services, strategically designed to support secure AI integration through comprehensive strategy, governance, application security, and education. By reinforcing AI policies and guidelines, Optiv not only minimizes risks but also unlocks business value and accelerates innovation. Positioned as an expert partner, Optiv provides end-to-end support across various AI use cases, ensuring that businesses can safely harness AI's potential while safeguarding data and system integrity. As industries shift towards AI-driven solutions, how are you approaching AI security in your organization? What measures do you believe are essential to ensure a secure AI future? Your thoughts are valuable—join the conversation!https://lnkd.in/eqXb73Ap

🚀 Navigating the AI Era: Challenges for IT Managers As we dive deeper into the era of Artificial Intelligence, IT Managers are encountering a unique set of challenges. Here’s a closer look at the hurdles and how to address them: 🔍 Integration Complexity Challenge: Integrating AI technologies with existing systems can be daunting. Legacy systems may not easily align with new AI solutions. Example: A financial institution implementing AI for fraud detection must ensure seamless integration with their traditional transaction processing systems. 🔒 Security Concerns Challenge: AI introduces new vulnerabilities, from data breaches to malicious attacks targeting AI systems. Example: An e-commerce company using AI for personalized recommendations must safeguard against potential data leaks or misuse. 📊 Data Management Challenge: AI relies on vast amounts of data, raising concerns about data quality, storage, and governance. Example: A healthcare provider using AI for patient diagnostics needs to ensure accurate and secure data handling to avoid diagnostic errors. 🧩 Talent Gap Challenge: There’s a shortage of skilled professionals who understand both AI technologies and IT management. Example: An IT department looking to implement AI-driven customer support tools might struggle to find personnel with the right blend of AI expertise and IT management skills. 🛠️ Ongoing Training Challenge: Continuous learning and adaptation are necessary to keep pace with rapid AI advancements. Example: IT Managers must regularly update their skills and knowledge to effectively manage evolving AI tools and technologies. 💡 How to Overcome These Challenges: Invest in Training: Ensure your team stays updated with the latest AI developments and best practices. Strengthen Security Protocols: Implement robust security measures to protect AI systems and data. Foster Collaboration: Work closely with AI specialists to ensure smooth integration and deployment. Embracing these challenges with a proactive approach will pave the way for successful AI integration and drive innovation within your organization. 🌟 #ITManagement #ArtificialIntelligence #TechChallenges #DataSecurity #AIIntegration #Leadership #ITManager

Struggling to make sense of all your company's unstructured data? Want to use AI but worried about security risks in a multi-team setup? The answer lies in vector stores and knowledge bases. These technologies transform the way you manage and analyze data: • 𝗩𝗲𝗰𝘁𝗼𝗿 𝗦𝘁𝗼𝗿𝗲𝘀: Enable lightning-fast similarity searches across your data. • 𝗞𝗻𝗼𝘄𝗹𝗲𝗱𝗴𝗲 𝗕𝗮𝘀𝗲𝘀: AI-powered Assistants – ask questions, get instant insights. • 𝗦𝗲𝗰𝘂𝗿𝗲 𝗖𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻: Teams maintain data privacy while having the option for controlled sharing. 𝗧𝗵𝗲 𝗞𝗮𝘁𝗼𝗻𝗶𝗰 𝗦𝗼𝗹𝘂𝘁𝗶𝗼𝗻 The Katonic AI Generative AI Platform streamlines the creation and management of knowledge bases within large organizations. We help you: • 𝗚𝗲𝘁 𝘂𝗽 𝗮𝗻𝗱 𝗿𝘂𝗻𝗻𝗶𝗻𝗴 𝗾𝘂𝗶𝗰𝗸𝗹𝘆 • 𝗢𝗽𝘁𝗶𝗺𝗶𝘇𝗲 𝗔𝗜 𝗽𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 • 𝗠𝗮𝗶𝗻𝘁𝗮𝗶𝗻 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗹𝗲𝘃𝗲𝗹 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 Learn more in our new blog post: https://lnkd.in/dy27GqHx Ready to unlock your data's potential and empower secure collaboration? Contact Katonic AI today!