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Scooped by
JC Gaillard
onto Artificial Intelligence and Cybersecurity May 8, 2018 2:18 AM
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Enterprise attacks are far more ruthless today than ever. This puts more pressure on enterprise to sanitize and secure their footprint. See how modern technologies like AI and machine learning are being used to create front-of-the-line defenses to protect enterprises and their data from prying eyes.
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Measuring AI model security gets complex, fast, thanks to a growing patchwork of benchmarks targeting different variables in the equation.
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The more significant impact of AI is that it is exposing a weakness that has existed within vulnerability management programs for years: severity has never been the same thing as risk.
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AI agent permissions outrun controls: 70% of organizations let agents act without human sign-off, but only 49% can audit every agent action.
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Stolen AI API keys are powering an autonomous hacking tool sold on criminal forums, Bitsight reports, as AI agent chatter jumped 67 percent.
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Agentic systems are exposing years of unresolved data debt by turning ambiguity into operational exposure.
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AI vulnerability patching tools can stop a crash without fixing the bug, and benchmark scores often miss the difference, researchers found.
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Just over half of 200 leaders in ArmorCode's vulnerability management survey expect to struggle with program complexity if nothing changes.
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AI is widening the gap between the CVEs teams find and the ones they can fix. Closing it means asking which flaws attackers can actually reach.
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AI and quantum are both emerging technologies, and are both challenging established cyber benchmarks daily.
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More than 80,000 proxy servers have been observed cloaking the origin of traffic to frontier models, likely using AI credentials harvested via information stealers, phishing campaigns, and supply-chain attacks.
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Frontier labs OpenAI and Anthropic oversold their 'rogue AI' hacks to push for federal regulations that would disadvantage open models, competitors claim.
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CLOSEDQUORUM queries multiple models, tallies their decisions and automatically executes the winning action, eliminating the need for continuous attacker commands.
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As attackers use AI and automation to move from access to impact faster than traditional SOC workflows were designed to handle, security teams need technology that can operate at comparable speed.
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Here's how validation answers the questions boards have about cyber risk.
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AI is affecting cybersecurity jobs and career development as employers cut roles and seek skills to manage threats and check tools.
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Unsanctioned AI use is epidemic, and agentic AI expands the risk. Here are four proven tactics that work better together.
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AI-powered penetration testing can uncover complex attack paths, but safety restrictions and network limitations leave many assets and vulnerabilities untested.
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Non-human identities now outnumber human accounts 45 to 1, leaving critical security gaps. Limit the vulnerabilities and enact governance with these tips.
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With flaw discoveries mounting, adversarial simulation and continuous pen testing can help CISOs identify which vulnerabilities matter most. New autonomous tooling is here to lend a hand but skilled practitioners remain critical.
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Enterprises regularly rigorously monitor human employees, while autonomous AI agents quietly operate with broad privileges.
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As AI accelerates vulnerability discovery and exploitation, so-called virtual patching still comes down to defense-in-depth and strong application security fundamentals.
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LLMs are software systems requiring proper containment control, not sentient actors with malicious intent.
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Crisis management experts say that AI is vital tool for stress-testing crisis management plans that can reveal critical blind spots that human teams may overlook.
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Software has never been easier to build, or easier to get wrong at scale.
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Security leaders and recruiters describe a job market that’s consolidating authority, raising the bar for judgment, and squeezing the entry-level roles that once trained the next generation of practitioners.
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