Benefits of AI in Cyber Defense

Revolutionizing ethical hacking—AI is changing the way we protect against cyber threats. Gone are the days of time-consuming manual assessments. With AI-driven tools, ethical hackers can identify and patch vulnerabilities faster and more effectively than ever before. Here’s how AI is leading the charge in transforming ethical hacking: 1️⃣ Automated Vulnerability Scanning ↳ Tools like Senteon, CheckRed and CYRISMA automate the scanning process, quickly identifying security gaps such as SQL injections. This allows for more frequent checks and quicker fixes. 2️⃣ Enhanced Threat Detection ↳ AI analyzes vast data sets to detect abnormal patterns, adapting to new attack methods and enabling preemptive threat responses. 3️⃣ Natural Language Processing for Command Execution ↳ Tools like Nebula allow ethical hackers to input commands in simple language, improving speed and accessibility. 4️⃣ Intelligent Risk Prioritization ↳ AI ranks vulnerabilities by severity, helping hackers focus on the most critical threats first and allocate resources effectively. 5️⃣ Continuous Learning and Improvement ↳ AI systems evolve by learning from past data and incidents, staying ahead of emerging threats and improving security responses over time. AI is a game-changer in cybersecurity, making the fight against digital threats more efficient and proactive. Where do you see AI taking cybersecurity next? Let’s chat in the comments!

The Impact of Security AI Tools On the flip side of all the pessimism and fear around AI, there's also a lot of hope around the promise of AI security tooling. That said, a lot is still to be discovered, such as speed, accuracy, and productivity gains. This is an interesting study on that very topic from the team at Microsoft, conducting "Randomized Controlled Trial for Microsoft Security Copilot" The team looked at: - Efficiency gains from using Security Co-Pilot using M365 Security, including speed and quality - Four task were examined: Incident Summarization, Script Analyzer, Incident Reporting and Guided Response Key Findings included subjects being: ➡ 44% more accurate in answering about scripts used by attackers ➡ 151% more accurate in answering questions about incidents ➡ 73% more accurate in answering questions about remediation steps While some may be skeptical due to the source, they lay out the data well, as well as cite similar studies from other groups, including outside of Microsoft. Attackers will be looking to maximize their efficiency by leveraging AI - Defenders must do the same! #cybersecurity #ai #software #ciso

𝗗𝗮𝘆 𝟭𝟮: 𝗟𝗲𝘃𝗲𝗿𝗮𝗴𝗲 𝗔𝗜/𝗚𝗲𝗻𝗔𝗜 𝘁𝗼 𝗳𝗶𝗴𝗵𝘁 𝗮𝗱𝘃𝗲𝗿𝘀𝗮𝗿𝗶𝗲𝘀 One of the most pressing challenges in cybersecurity today is the global talent shortage, with 𝗮𝗽𝗽𝗿𝗼𝘅𝗶𝗺𝗮𝘁𝗲𝗹𝘆 𝟯.𝟱 𝗺𝗶𝗹𝗹𝗶𝗼𝗻 𝘂𝗻𝗳𝗶𝗹𝗹𝗲𝗱 𝗽𝗼𝘀𝗶𝘁𝗶𝗼𝗻𝘀 𝗽𝗿𝗼𝗷𝗲𝗰𝘁𝗲𝗱 𝗯𝘆 𝟮𝟬𝟮𝟱. This gap poses substantial risks, as unfilled roles lead to increased vulnerabilities, cyberattacks, data breaches, and operational disruptions. While there are learning paths like 𝗩𝗶𝘀𝗮’𝘀 𝗣𝗮𝘆𝗺𝗲𝗻𝘁𝘀 𝗖𝘆𝗯𝗲𝗿𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗰𝗲𝗿𝘁𝗶𝗳𝗶𝗰𝗮𝘁𝗶𝗼𝗻 𝗽𝗿𝗼𝗴𝗿𝗮𝗺 to help aspiring cyber professionals upskill and build careers, Generative AI (GenAI) and Agentic AI offers a scalable solution by augmenting existing teams. Together, they can handle repetitive tasks, automate workflows, enhance incident triaging, and automate code fixes and vulnerability management, enabling smaller teams to scale and maintain robust security postures. Additionally, they enhance cybersecurity efforts by improving defenses while keeping humans in the loop to make critical, informed decisions. Here are few concept about GenAI in Cybersecurity that I’m particularly excited about: 1. Reducing Toil and Improving Team Efficiency GenAI can significantly reduce repetitive tasks, enabling teams to focus on strategic priorities: • GRC : Automates risk assessments, compliance checks, and audit-ready reporting. • DevSecOps: Integrates AI-driven threat modeling and vulnerability scanning into CI/CD pipelines. • IAM : Streamlines user access reviews, provisioning, and anomaly detection. 2. Extreme Shift Left GenAI can rapidly enhance “Secure-by-Design” into development processes by: • Detecting vulnerabilities during coding and providing actionable fixes. • Automating security testing, including fuzzing and penetration testing. 3. Proactive Threat Hunting and Detection Engineering GenAI can enhance threat hunting by: • Analyzing logs and sensor data to detect anomalies. • Correlating data to identify potential threats. • Predicting and detecting attack vectors to arm the sensors proactively. 4. Enabling SOC Automation Security Operations Centers (SOCs) can benefit from GenAI by: • Automating false positive filtering and alert triaging. • Speeds up analysis and resolution with AI-powered insights. • Allowing analysts to concentrate on high-value incidents and strategic decision-making. 𝟱. Enhancing Training and Awareness • Delivering tailored training simulations for developers and business users. • Generating phishing campaigns to educate employees on recognizing threats. In 2025, I am excited about the transformative opportunities that lie ahead. Our focus remains steadfast on innovation and resilience, particularly in leveraging the power of Gen/Agentic AI to enhance user experience, advance our defenses and further strengthen the posture of the payment ecosystem.   #VISA #Cybersecurity #PaymentSecurity #12DaysofCybersecurity #AgenticAI

Many people use AI in Security and Securing AI interchangeably. I am sharing here the difference between these with use cases. -- AI in CyberSecurity - Using AI to Enhance Cybersecurity. 6 Possible use cases: 1. Anomaly Detection — Not just signature based attack but using AI to identify malicious network traffic — https://lnkd.in/gCWfz9KK 2. Phishing Detection — Using AI to detect phishing emails through behavioral, content and threat network analysis — https://www.tessian.com/ 3. Fraud Detection — AI monitors financial transactions to identify and prevent fraud. — https://lnkd.in/gf9HDTJB 4. Malware Detection — AI algorithms can detect and classify new strains of malware. — https://lnkd.in/gDx6yuaR 5. Threat Modelling — AI can assist in creating dynamic threat models for applications by analyzing historical attack data and identifying potential threat vectors. — https://lnkd.in/gpxstAnn 6. Secure Code Review — AI can be used to automatically review code for vulnerabilities, improving the efficiency and effectiveness of code audits. — https://lnkd.in/gvCXNHMA -- Securing AI - Protecting LLMs from Threats. 4 use cases 1. Prompt Injection — Issues with crafted inputs leading to unauthorized actions by LLMs — https://lnkd.in/gzFNGXUi 2. Training data extraction — Attacks that can reveal secret system prompts — https://lnkd.in/g8STNwj5 3. Training Data Poisoning — Compromises in the integrity of training data for LLMs — https://lnkd.in/guF6_xHT 4. Model Denial of Service — Attacks designed to overload LLMs with resource-intensive operations — https://lnkd.in/gxexRErF -- There is just lot of incredible product building and development happening in this space. I will be sharing more of my views on these topics here.

GenAI technologies offer the promise of enhancing security processes by automating the identification and remediation of internal vulnerabilities, streamlining security operations, and providing decision-makers with insights derived from complex data analyses. By learning from interactions, behaviors, and past incidents, GenAI can anticipate potential security breaches before they occur, enabling proactive rather than reactive measures. Moreover, GenAI's capability to generate training materials and simulate threat scenarios in real-time presents an opportunity to improve security awareness among employees at all levels. As we embrace GenAI in cybersecurity, it's important for organizations to balance innovation with ethical considerations, ensuring that the use of AI respects privacy, consent, and transparency. The potential of GenAI to revolutionize cybersecurity is significant, promising to protect us from external hackers and strengthen our defenses from within, making our digital systems safer. #GenerativeAI #CybersecurityInnovation #DigitalResilience

🙋♀️ I am back with yet another post on AI in Cyber! This past week, I attended a session hosted by Women in CyberSecurity (WiCyS) and SentinelOne. The star of the show was Purple AI, an AI security analyst that works 24/7 to keep your organization safe. What sets it apart is its ability to use natural language processing to understand and respond to complex security queries. Imagine asking, "Am I being targeted by FIN7?" and getting an instant, comprehensive analysis drawn from all your organizational data sources. Here are some key features that caught my attention: 🏇 Faster threat hunting and investigations 🏇 Simplification of threat hunting processes 🏇 Reduction in complexity for security operations I believe AI based security analysts like these have the ability to accelerate SecOps with their speedy analyses, auto-summaries, and suggested queries.   Well, does this mean we don’t need human analysts anymore? Absolutely not. With the power of such tools, humans can focus on what they do best, which is applying critical thinking to complex security challenges. By combining the speed and scalability of AI with the nuanced understanding of human experts, we're entering a new era of cybersecurity defense and I'm super excited to be a part of this transformation. What are your thoughts on AI-powered security tools like Purple AI? #AIinCybersecurity #PurpleAI #ThreatHunting #WomenInTech #SentinelOne #aiincyber #WomenInTech #WomenInCybersecurity #WiCyS #securityoperations #secops #AISecuritytools #CyberIntelligence Adriana Corona Mani Keerthi N Betty Cheng

Cybersecurity scaling is here. Vulnerabilities can now be scaled with Agentic AI See Video Check out the NIM pre trained models + Nemo framework video. Nvidia has introduced several innovative solutions to scale cybersecurity using Agentic AI, addressing critical concerns like trust, safety, security, and compliance in AI applications. These solutions are designed to help enterprises improve the safety, precision, and scalability of their generative AI applications, particularly in the realm of cybersecurity. NIM Agent Blueprint for Cybersecurity Nvidia has launched a NIM Agent Blueprint for cybersecurity, which combines several of Nvidia’s advanced technologies: 1. Morpheus cybersecurity AI framework 2. Nvidia cuVS 3. Rapids data analytics This blueprint is designed to accelerate the detection of vulnerabilities (CVEs) at scale. It enables enterprises to use generative AI to digest information and explain vulnerabilities using natural language, allowing companies to create agents for cybersecurity workflows. NeMo Guardrails Microservices Nvidia has introduced three new NIM (Nvidia Inference Microservices) for NeMo Guardrails, which are crucial for maintaining controlled behavior in AI agents: 1. Content safety NIM microservice: This safeguards AI against generating biased or harmful outputs, ensuring responses align with ethical standards. 2. Topic control NIM microservice: It keeps conversations focused on approved topics, avoiding digression or inappropriate content. 3. Jailbreak detection NIM microservice: This adds protection against jailbreak attempts, helping maintain AI integrity in adversarial scenarios. These microservices leverage small language models (SLMs) with lower latency than LLMs, making them ideal for scaling AI applications in resource-constrained or distributed environments. Key Benefits for Cybersecurity 1. Improved Safety: The content safety microservice, trained on the Aegis Content Safety Dataset, helps prevent AI from generating harmful or biased outputs. 2. Enhanced Focus: The topic control microservice keeps AI agents on track, preventing them from discussing unauthorized topics or competitor products. 3. Increased Security: The jailbreak detection microservice, built on Nvidia Garak, protects against attempts to bypass an AI agent’s safeguards. 4. Scalability: These solutions are designed to operate efficiently in various environments, including healthcare, automotive, and manufacturing sectors. 5. Customization: NeMo Guardrails allow for customization and extension of rails to meet specific industry + geographical regulatory requirements. By implementing these Nvidia solutions, enterprises can create more secure, trustworthy AI agents that provide safe, appropriate responses within context-specific guidelines. This approach not only boosts customer satisfaction + trust but also addresses the critical cybersecurity challenges faced in the era of AI-driven applications. #CEO #CISO #Cybersecurity

As a former CISO, I’ve witnessed the struggles of even the best security analysts. The attack surface grows exponentially. Alert fatigue leads to missing real threats and eventually causes burnout. The reality is that your best analyst works 8 hours a day, while cyber threats don’t operate in shifts. With Agentic AI SOC Analysts, you are covered 24/7, without distractions, fatigue, or oversight. Here’s how AI-powered security transforms SOC operations: - Always On – AI never takes breaks, ensuring threats don’t slip through the cracks. - Cuts Through Noise – Reduces false positives so teams can focus on real threats. - Faster Investigations – Automates triage and analysis in seconds, not hours. - Scales Without Burnout – Expands SOC capacity without adding headcount. - No Playbooks Required – Works autonomously, learning and adapting like a top analyst. It’s time to rethink cybersecurity. AI isn’t just a tool - it’s the future of SOC efficiency. #Cybersecurity #SOC #AI #ThreatDetection #SecurityAutomation