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Cybersecurity venture funding 2025 climbs as capital concentrates, AI security expands, and seed activity aligns with enterprise budgets. 
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GenAI isn’t the real problem — unmanaged data is. Smarter XDR-based protection lets teams innovate without leaking what matters most.
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Organizations are expanding capability faster than they are expanding control. The teams that perform best in 2026 will be those that execute well under pressure—detecting earlier, responding faster, learning continuously and automating what works.
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The familiar playbook of identifying known threats and blocking known patterns is becoming less reliable by the day.
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Resilience requires first securing AI foundations and then scaling AI-driven defence capabilities.
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From
fortune
Why OpenAI’s latest coding breakthrough is forcing the company to rethink how—and how fast—it can deploy its most powerful models.
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Abandoned AI projects and pilots also create unforeseen and often undetectable vulnerabilities.
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AI agents are now executing regulated actions, reshaping how compliance controls actually work.
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From
www
Analysis by Sonatype found that AI is giving attackers new opportunities to target victims
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From
dzone
AI-driven development is outpacing security. This piece shows where AI security tools help, where they fail, and how teams can use them responsibly.
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AI is evolving at a rapid pace, and the uptake of Generative AI (GenAI) is revolutionising the way humans interact and leverage this technology.
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Misaligned agents are just one layer of the AI security challenge that startup Witness AI is trying to solve. It detects employee use of unapproved tools, blocks attacks, and ensures compliance.
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AI agents AppSec risk is rising as no-code automations bypass SDLC controls, create blind spots, and trigger internal breaches at scale.
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Agentic AI’s near-term value is already real in internal workflows; unlocking the next wave depends on standardizing how we secure, orchestrate, and ship agents
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CISOs and IT leaders require practical, risk-based approaches to evaluating AI's role as the technology continues to evolve and integrate into cybersecurity operations.
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AI agents take on greater responsibility, but CISOs remain accountable for outcomes, and even for inaction when organizations fail to adopt and govern machine-speed security capabilities.
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From
www
Agentic AI brings a new set of security risks that go beyond those introduced by more straightforward large language models (LLMs), generative AI (gen AI) chatbots or other forms of artificial intelligence.
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Enterprises rush to deploy autonomous systems as securing AI agents lags, exposing gaps in identity, governance, visibility, and control.
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Most security leaders believe they know where their sensitive data lives and how it is protected. That confidence is increasingly misplaced.
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Exponential expansion of autonomous agents in the enterprise may expand enterprise threat surfaces to an almost unmanageable degree — especially given poor foundations for non-human identity oversight.
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AI can strengthen cyber defenses, but only when paired with human oversight, transparent data, and strong governance, not blind trust in autonomous systems.
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Emerging NIST guidance suggests that the long-standing practice of treating AI as “just software” for cybersecurity purposes is giving way to more novel approaches to managing AI risks.
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In the coming year, organizations will no longer measure cyber resilience by whether a breach occurred. They will measure it by how quickly it was detected, how precisely it was isolated, and how confidently systems were restored.
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Security execs increasingly see AI as the means to supercharge their security operations, but the rush to embed AI in security tools can create myriad pitfalls, experts warn.
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AI adoption depends on application modernization, security alignment, and disciplined foundations that support scalable AI programs.
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