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Scooped by
JC Gaillard
onto Artificial Intelligence and Cybersecurity August 25, 2019 2:19 AM
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Businesses are in a cybersecurity arms race - harnessing AI to mitigate potential threats, but what if it falls into the wrong hands?
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Shadow AI hidden in SaaS apps is driving a surge in cyberattacks, with OAuth token abuse and agentic AI enabling cascading data breaches.
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From
ortecha
Autonomous AI agents are entering enterprise systems with real permissions and decision-making capability. As organisations automate workflows, a new type of insider risk is emerging — one that governance frameworks are only beginning to address.
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AI coding agents introduced vulnerabilities in 87% of pull requests across Claude, Codex, and Gemini builds, exposing access control gaps.
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From
www
Google's latest threat report warns that third-party tools are now prime targets for attackers - and businesses have just days to keep them secure.
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Although much is said about the roles of entry-level analysts, CISOs should also focus on governance, playbooks and more to ensure that the SOC and the team is ready for AI.
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Narrow “shift left” has failed at AI scale. Move from developer-led fixes to AppSec-managed automation that triages findings and delivers tested pull-request fixes so teams can safely manage AI-generated code.
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As attackers use automation and AI to outpace traditional tools and people, our approach to cybersecurity must fundamentally change.
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From
venturebeat
This is the second publicly disclosed Claude-enabled cyberattack in less than a year.
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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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From
www
Developments in The Prompt Economy over the past few weeks has been moving from retail and infrastructure issues to a focus on financial services.
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From
cybernews
No one asked them to do it. No adversarial prompting was employed. But these AI agents, deployed for routine enterprise tasks in a test, still went rogue and autonomously hacked the systems they were operating in.
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AI makes it easy to find executive PII online. Security teams must reduce digital exposure before attackers use AI to surface sensitive data.
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From
www
Automation and Agents Could Tip the Scales if Organizations Manage the Sprawl
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Humans at the controls. AI doing the boring work. Everything coming together—but faster, more accurately, and with a sense of judgement at the helm.
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From
www
Think your encrypted backups are safe? AI-driven ransomware now infiltrates networks, corrupts recovery points, and silently targets backup systems before you ever realize your data protection strategy has failed.
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From
www
AI is neither a miracle nor a menace. It is not a single story. It is a combination of genuine breakthroughs, unmanaged risks and inflated expectations, and whether it becomes a boon, a bane or a bubble depends almost entirely on how leaders choose to govern it.
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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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