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									AI &amp; ML - eTechIntel Community				            </title>
            <link>https://consulty247.com/tech-community/ai-ml/</link>
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                        <title>After Mythos: AI-Driven Exploits &amp; the Future of Exposure Management — Webinar Recording Now Available</title>
                        <link>https://consulty247.com/tech-community/ai-ml/after-mythos-ai-driven-exploits-exposure-management-webinar-recording-now-available/</link>
                        <pubDate>Thu, 21 May 2026 16:01:13 +0000</pubDate>
                        <description><![CDATA[After Mythos: AI-Driven Exploits &amp; the Future of Exposure Management — Webinar Recording Now Available
  

  
    The on-demand recording of our recent cybersecurity session is now available...]]></description>
                        <content:encoded><![CDATA[<div class="webinar-post">

  <!-- Header -->
  <h1 class="title">
    After Mythos: AI-Driven Exploits & the Future of Exposure Management — Webinar Recording Now Available
  </h1>

  <p class="subtitle">
    The on-demand recording of our recent cybersecurity session is now available. The discussion explores how AI-driven exploit development is reshaping vulnerability lifecycles, compressing remediation timelines, and redefining modern exposure management strategies across enterprise environments.
  </p>

  <hr />

  <!-- Thank You Section -->
  <section class="thank-you">
    <h2>Thank You for Joining Us</h2>
    <p>
      Thank you to everyone who attended the live session. Your participation contributed to a valuable discussion on how AI is influencing the threat landscape and accelerating the evolution of exposure management practices.
    </p>
  </section>

  <!-- Missed Section -->
  <section class="missed">
    <h2>Missed the Live Session?</h2>
    <p>
      If you were unable to attend, the full webinar recording is now available on demand. You can access it at any time to revisit key insights shared during the session and understand how security teams are adapting to AI-driven threats.
    </p>
  </section>

  <!-- Webinar Overview -->
  <section class="overview">
    <h2>About the Session</h2>
    <p>
      This expert-led session examines how autonomous and AI-assisted systems are accelerating exploit development, significantly reducing the time between vulnerability disclosure and exploitation, and challenging traditional patch management approaches.
    </p>

    <ul>
      <li>How AI is reshaping exploit development and attack automation</li>
      <li>The rapid compression of vulnerability exploitation timelines</li>
      <li>The transition from periodic patching to continuous exposure management</li>
      <li>Improving visibility across complex enterprise attack surfaces</li>
      <li>Risk-based prioritization strategies for modern vulnerability programs</li>
    </ul>
  </section>

  <!-- Speaker -->
  <section class="speaker">
    <h2>Featured Speaker</h2>
    <p>
      <strong>Melissa Bischoping</strong><br/>
      Head of Threat Research & Intelligence, Tanium
    </p>
  </section>

  <!-- CTA -->
  <section class="cta">
    <h2>Watch the Recording</h2>
    <p>
      Access the full webinar recording below to explore the complete discussion and key insights. Share it with your security, IT, or risk teams to support ongoing conversations around AI-driven threat evolution and exposure management maturity.
    </p>

    <a href="https://etechintel.com/after-mythos-ai-driven-exploits-the-future-of-exposure-management-webinar-recording/" class="btn-primary" target="_blank">
      ▶ Watch Webinar Recording
    </a>
  </section>

  <hr />

  <!-- Closing -->
  <section class="closing">
    <p>
      If you’d like to continue the discussion or have questions about the topics covered, feel free to reach out to our community team. We look forward to having you join us in our upcoming sessions.
    </p>

    <p><strong>Community Team</strong></p>
  </section>

</div>]]></content:encoded>
						                            <category domain="https://consulty247.com/tech-community/ai-ml/">AI &amp; ML</category>                        <dc:creator>etechinteladmin</dc:creator>
                        <guid isPermaLink="true">https://consulty247.com/tech-community/ai-ml/after-mythos-ai-driven-exploits-exposure-management-webinar-recording-now-available/</guid>
                    </item>
				                    <item>
                        <title>After Mythos: AI-Driven Exploits &amp; the Future of Exposure Management — Webinar Starts in 30 Minutes</title>
                        <link>https://consulty247.com/tech-community/ai-ml/after-mythos-ai-driven-exploits-the-future-of-exposure-management-webinar-starts-in-30-minutes/</link>
                        <pubDate>Thu, 21 May 2026 14:11:19 +0000</pubDate>
                        <description><![CDATA[&#x1f6a8; Webinar Starts in 30 Minutes
We’re excited to welcome everyone to today’s session, *After Mythos: AI-Driven Exploits &amp; the Future of Exposure Management*.
Join Melissa Bischopi...]]></description>
                        <content:encoded><![CDATA[<p>&#x1f6a8; Webinar Starts in 30 Minutes</p>
<p>We’re excited to welcome everyone to today’s session, *After Mythos: AI-Driven Exploits &amp; the Future of Exposure Management*.</p>
<p>Join Melissa Bischoping, Head of Threat Research and Intelligence at Tanium, as we explore how AI-driven exploit development is reshaping vulnerability management, exposure management, and enterprise cyber resilience strategies.</p>
<p>As organizations face increasingly automated attack ecosystems and compressed remediation timelines, this discussion will provide valuable insights into how security teams can strengthen operational readiness and stay ahead of evolving threats.</p>
<p>&#x1f517; Join the webinar here:</p>
<p>https://us06web.zoom.us/j/88037548665</p>
<p>See you soon!</p>
<p>&nbsp;</p>
<p>Best regards,</p>
<p>Community Team</p>]]></content:encoded>
						                            <category domain="https://consulty247.com/tech-community/ai-ml/">AI &amp; ML</category>                        <dc:creator>etechinteladmin</dc:creator>
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                    </item>
				                    <item>
                        <title>After Mythos: AI-Driven Exploits &amp; the Future of Exposure Management</title>
                        <link>https://consulty247.com/tech-community/ai-ml/after-mythos-ai-driven-exploits-future-of-exposure-management/</link>
                        <pubDate>Tue, 12 May 2026 23:47:18 +0000</pubDate>
                        <description><![CDATA[Hello Community Members,
  

  
    We are pleased to announce an upcoming cybersecurity webinar presented by 
    Tanium, focused on one of the most significant emerging challenges in enter...]]></description>
                        <content:encoded><![CDATA[<div style="font-family:Arial,Helvetica,sans-serif;font-size:15px;line-height:1.8;color:#1f2937;max-width:900px;margin:auto;">

  <p style="margin-top:0;">
    Hello Community Members,
  </p>

  <p>
    We are pleased to announce an upcoming cybersecurity webinar presented by 
    <strong>Tanium</strong>, focused on one of the most significant emerging challenges in enterprise security —
    <strong>AI-driven exploit development and the future of exposure management.</strong>
  </p>

  <p>
    As AI capabilities continue to evolve, the time between vulnerability disclosure and active exploitation is shrinking rapidly. Security teams that previously had weeks to assess and remediate vulnerabilities may soon have only days — or even hours — before exploitation attempts begin.
  </p>

  <p>
    This shift is forcing organizations to rethink traditional patching models, vulnerability prioritization, exposure visibility, and overall cyber resilience strategies in order to keep pace with increasingly automated threat environments.
  </p>

  <div style="
      background:#f8fafc;
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      padding:24px;
      margin:34px 0;
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  ">

    <h2 style="
        margin-top:0;
        margin-bottom:16px;
        color:#111827;
        font-size:25px;
        line-height:1.4;
        font-weight:700;
    ">
      Live Webinar
    </h2>

    <p style="
      margin:10px 0 18px 0;
      font-size:18px;
      color:#111827;
      line-height:1.7;
      font-weight:700;
    ">
      After Mythos: What AI-Driven Exploit Development Means for Patch & Exposure Management Programs
    </p>

    <div style="
      border-top:1px solid #e5e7eb;
      padding-top:16px;
    ">

      <p style="margin:8px 0;">
        📅 <strong>Date:</strong> 21 May 2026
      </p>

      <p style="margin:8px 0;">
        🕒 <strong>Time:</strong> 3:00 PM GMT | 4:00 PM CET | 11:00 AM EST
      </p>

      <p style="margin:8px 0;">
        🎙 <strong>Speaker:</strong> Melissa Bischoping — Head of Threat Research & Intelligence, Tanium
      </p>

    </div>

  </div>

  <h3 style="
      color:#111827;
      margin-bottom:14px;
      margin-top:36px;
      font-size:22px;
      font-weight:700;
  ">
    What Will Be Covered?
  </h3>

  <ul style="
      padding-left:22px;
      margin-top:12px;
  ">

    <li style="margin-bottom:12px;">
      How AI is accelerating exploit development and automated attack workflows
    </li>

    <li style="margin-bottom:12px;">
      Why vulnerability exposure windows are rapidly shrinking across enterprise environments
    </li>

    <li style="margin-bottom:12px;">
      The transition from periodic patching to continuous exposure management
    </li>

    <li style="margin-bottom:12px;">
      AI agents, enterprise attack surfaces, and emerging operational risks
    </li>

    <li style="margin-bottom:12px;">
      Modern vulnerability prioritization and risk-based remediation strategies
    </li>

    <li>
      How security teams can prepare for machine-speed, AI-driven threat environments
    </li>

  </ul>

  <h3 style="
      color:#111827;
      margin-bottom:14px;
      margin-top:40px;
      font-size:22px;
      font-weight:700;
  ">
    Who Should Attend?
  </h3>

  <p>
    This webinar is highly recommended for:
  </p>

  <ul style="
      padding-left:22px;
      margin-top:12px;
  ">

    <li style="margin-bottom:10px;">CISOs & Security Leaders</li>

    <li style="margin-bottom:10px;">Security Architects</li>

    <li style="margin-bottom:10px;">Vulnerability Management Teams</li>

    <li style="margin-bottom:10px;">SOC & Threat Intelligence Professionals</li>

    <li style="margin-bottom:10px;">Enterprise IT & Infrastructure Leaders</li>

    <li>Cybersecurity Researchers & Practitioners</li>

  </ul>

  <div style="
      background:#f9fafb;
      border:1px solid #e5e7eb;
      padding:22px;
      border-radius:8px;
      margin:34px 0;
  ">

    <p style="margin:0;">
      Organizations that fail to modernize exposure management and vulnerability response strategies may struggle to defend against increasingly automated and AI-accelerated attack campaigns. This session will provide practical insights into how enterprise security teams can improve operational readiness, cyber resilience, and remediation speed in rapidly evolving threat environments.
    </p>

  </div>

  <h3 style="
      color:#111827;
      margin-bottom:14px;
      margin-top:38px;
      font-size:22px;
      font-weight:700;
  ">
    Discussion Question
  </h3>

  <div style="
      background:#eff6ff;
      border:1px solid #bfdbfe;
      border-left:4px solid #2563eb;
      padding:18px 20px;
      border-radius:8px;
      margin-bottom:34px;
  ">

    <p style="margin:0;">
      How is your organization adapting patch management and exposure management strategies to prepare for AI-driven threats and increasingly compressed remediation timelines?
    </p>

  </div>

  <div style="margin:36px 0 22px 0;">

    <a href="https://etechintel.com/after-mythos-ai-driven-exploits-patch-management/"
       target="_blank"
       style="
          display:inline-block;
          color:#111827;
          text-decoration:none;
          font-size:18px;
          font-weight:700;
          letter-spacing:0.2px;
          border-bottom:2px solid #111827;
          padding-bottom:4px;
       ">
       Register for the Webinar →
    </a>

  </div>

  <p>
    As AI-driven exploitation capabilities continue to evolve, organizations must rethink traditional security operations and exposure management approaches to remain resilient against machine-speed threats and increasingly automated attack ecosystems.
  </p>

  <p>
    We encourage community members to register early and join this timely discussion with leading cybersecurity researchers and enterprise security professionals.
  </p>

  <p style="margin-top:26px;">
    Regards,<br>
    <strong>Community Team</strong>
  </p>

</div>]]></content:encoded>
						                            <category domain="https://consulty247.com/tech-community/ai-ml/">AI &amp; ML</category>                        <dc:creator>etechinteladmin</dc:creator>
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                    </item>
				                    <item>
                        <title>AI Infrastructure: The New Tech Backbone</title>
                        <link>https://consulty247.com/tech-community/ai-ml/infrastructure-behind-systems/</link>
                        <pubDate>Sun, 03 May 2026 21:33:05 +0000</pubDate>
                        <description><![CDATA[]]></description>
                        <content:encoded><![CDATA[]]></content:encoded>
						                            <category domain="https://consulty247.com/tech-community/ai-ml/">AI &amp; ML</category>                        <dc:creator>Brian Kirst</dc:creator>
                        <guid isPermaLink="true">https://consulty247.com/tech-community/ai-ml/infrastructure-behind-systems/</guid>
                    </item>
				                    <item>
                        <title>The Future of MLOps Explained</title>
                        <link>https://consulty247.com/tech-community/ai-ml/mlops-workflow-future/</link>
                        <pubDate>Sun, 03 May 2026 18:49:22 +0000</pubDate>
                        <description><![CDATA[MLOps, or machine learning operations, is becoming essential for managing AI systems at scale. It combines development, deployment, and monitoring processes to ensure that models perform rel...]]></description>
                        <content:encoded><![CDATA[<p>MLOps, or machine learning operations, is becoming essential for managing AI systems at scale. It combines development, deployment, and monitoring processes to ensure that models perform reliably in real-world environments.</p><p>As AI adoption grows, the need for robust MLOps practices is increasing. Organizations must manage model versions, track performance, and ensure consistent results.</p><h3>From Deployment to Maintenance</h3><p>MLOps is not just about launching models—it’s about maintaining them over time. Continuous monitoring and updates are required to keep models effective.</p><p>This discipline is critical for turning AI from experimental projects into scalable solutions.</p>]]></content:encoded>
						                            <category domain="https://consulty247.com/tech-community/ai-ml/">AI &amp; ML</category>                        <dc:creator>Jim Barrier</dc:creator>
                        <guid isPermaLink="true">https://consulty247.com/tech-community/ai-ml/mlops-workflow-future/</guid>
                    </item>
				                    <item>
                        <title>Why Fine-Tuning Is the Key to Better AI</title>
                        <link>https://consulty247.com/tech-community/ai-ml/model-optimization-secrets/</link>
                        <pubDate>Sun, 03 May 2026 14:07:36 +0000</pubDate>
                        <description><![CDATA[Fine-tuning is a critical step in improving AI model performance. It involves adapting a pre-trained model to a specific task or dataset, allowing it to produce more relevant and accurate ou...]]></description>
                        <content:encoded><![CDATA[<p>Fine-tuning is a critical step in improving AI model performance. It involves adapting a pre-trained model to a specific task or dataset, allowing it to produce more relevant and accurate outputs.</p><p>This process requires less time and resources than training a model from scratch, making it a practical approach for many applications.</p><h3>Customization Matters</h3><p>Fine-tuning enables organizations to tailor AI systems to their unique needs, enhancing their effectiveness.</p><p>By focusing on domain-specific data, models can deliver more precise and valuable results.</p>]]></content:encoded>
						                            <category domain="https://consulty247.com/tech-community/ai-ml/">AI &amp; ML</category>                        <dc:creator>Steve Matecki</dc:creator>
                        <guid isPermaLink="true">https://consulty247.com/tech-community/ai-ml/model-optimization-secrets/</guid>
                    </item>
				                    <item>
                        <title>The Hidden Costs of Building AI Models</title>
                        <link>https://consulty247.com/tech-community/ai-ml/true-cost-of-building-models/</link>
                        <pubDate>Sun, 03 May 2026 10:28:51 +0000</pubDate>
                        <description><![CDATA[Building AI models involves significant costs that are often overlooked. Beyond development, expenses include data acquisition, storage, computational resources, and ongoing maintenance. The...]]></description>
                        <content:encoded><![CDATA[<p>Building AI models involves significant costs that are often overlooked. Beyond development, expenses include data acquisition, storage, computational resources, and ongoing maintenance. These costs can quickly add up, especially for large-scale projects.</p><p>Additionally, there are indirect costs such as talent acquisition, infrastructure setup, and compliance requirements.</p><h3>More Than Development</h3><p>Organizations must consider the full lifecycle of AI systems, including updates, monitoring, and scaling.</p><p>Ignoring these factors can lead to budget overruns and reduced return on investment.</p>]]></content:encoded>
						                            <category domain="https://consulty247.com/tech-community/ai-ml/">AI &amp; ML</category>                        <dc:creator>Shari Hammer</dc:creator>
                        <guid isPermaLink="true">https://consulty247.com/tech-community/ai-ml/true-cost-of-building-models/</guid>
                    </item>
				                    <item>
                        <title>How AI Systems Learn From Data</title>
                        <link>https://consulty247.com/tech-community/ai-ml/data-learning-process/</link>
                        <pubDate>Sun, 03 May 2026 07:56:08 +0000</pubDate>
                        <description><![CDATA[AI systems learn by identifying patterns in data through training processes that adjust model parameters. By analyzing examples, the system gradually improves its ability to make predictions...]]></description>
                        <content:encoded><![CDATA[<p>AI systems learn by identifying patterns in data through training processes that adjust model parameters. By analyzing examples, the system gradually improves its ability to make predictions or decisions.</p><p>This learning process involves multiple iterations, where the model’s performance is evaluated and refined. Over time, the system becomes more accurate and reliable.</p><h3>Continuous Learning</h3><p>AI systems are not static—they can be updated and improved as new data becomes available. This allows them to adapt to changing conditions and maintain relevance.</p><p>Understanding how AI learns is essential for building effective and trustworthy systems.</p>]]></content:encoded>
						                            <category domain="https://consulty247.com/tech-community/ai-ml/">AI &amp; ML</category>                        <dc:creator>Harry Manesis</dc:creator>
                        <guid isPermaLink="true">https://consulty247.com/tech-community/ai-ml/data-learning-process/</guid>
                    </item>
				                    <item>
                        <title>The Rise of Open-Source AI Models</title>
                        <link>https://consulty247.com/tech-community/ai-ml/open-model-movement/</link>
                        <pubDate>Sun, 03 May 2026 03:12:44 +0000</pubDate>
                        <description><![CDATA[Open-source AI models are transforming the industry by making advanced technology accessible to a wider audience. Developers, researchers, and organizations can now build on existing models,...]]></description>
                        <content:encoded><![CDATA[<p>Open-source AI models are transforming the industry by making advanced technology accessible to a wider audience. Developers, researchers, and organizations can now build on existing models, accelerating innovation and reducing development costs.</p><p>This collaborative approach fosters transparency and encourages experimentation, allowing the community to improve models collectively.</p><h3>Democratizing AI</h3><p>Open-source initiatives are lowering barriers to entry, enabling smaller companies and independent developers to compete with larger organizations.</p><p>As adoption grows, open-source models are becoming a powerful force in shaping the future of AI.</p>]]></content:encoded>
						                            <category domain="https://consulty247.com/tech-community/ai-ml/">AI &amp; ML</category>                        <dc:creator>Wayne Jeveli</dc:creator>
                        <guid isPermaLink="true">https://consulty247.com/tech-community/ai-ml/open-model-movement/</guid>
                    </item>
				                    <item>
                        <title>AI Models Are Getting Smaller—and Smarter</title>
                        <link>https://consulty247.com/tech-community/ai-ml/compact-model-evolution/</link>
                        <pubDate>Sun, 03 May 2026 00:39:27 +0000</pubDate>
                        <description><![CDATA[Recent advancements in AI have focused on creating smaller, more efficient models that deliver high performance without requiring massive computational resources. These models are optimized ...]]></description>
                        <content:encoded><![CDATA[<p>Recent advancements in AI have focused on creating smaller, more efficient models that deliver high performance without requiring massive computational resources. These models are optimized to run on edge devices, making AI more accessible and scalable.</p><p>Techniques such as model compression, pruning, and distillation allow developers to reduce model size while maintaining accuracy. This shift is enabling real-time applications in areas like mobile apps, IoT devices, and embedded systems.</p><h3>Efficiency Over Scale</h3><p>The trend toward smaller models reflects a growing emphasis on efficiency rather than sheer size. Organizations are prioritizing solutions that balance performance with cost and speed.</p><p>This evolution is making AI more practical for everyday use, expanding its reach across industries.</p>]]></content:encoded>
						                            <category domain="https://consulty247.com/tech-community/ai-ml/">AI &amp; ML</category>                        <dc:creator>David Kruglov</dc:creator>
                        <guid isPermaLink="true">https://consulty247.com/tech-community/ai-ml/compact-model-evolution/</guid>
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