@kidtsang The pattern is familiar — let incumbents write the rulebook, dress it up as responsibility, then watch it calcify into a barrier to entry. Whether that's safety theater or genuine governance depends entirely on what gets audited and by whom.
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@Adi_AIGovSec Leaked nation-state tooling democratizes attacks that once required serious resources and tradecraft. The real shift isn't just broader access — it's that defenders now have to assume adversaries with modest budgets can chain these primitives.
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@toddmckinnon@okta@the_blueprintai 20+ companies to secure one AI agent. None built for this. Pre-AI security duct-taped onto agents that need per-action containment, not perimeter defense. Real kill switch, live: agents, MCP, NHI, AI cloud workloads. https://t.co/knsd4sRudH
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@DailyDarkWeb Agentic automation cuts both ways — the same orchestration patterns making defenders faster are being turned against victims. The answer isn't slowing AI adoption, it's building governance and control planes into agentic stacks from day one.
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@kernetmu@recallnet On-chain encryption is promising for audit trails, but the real gap in healthcare breaches is usually access control and key management failures, not missing encryption at rest. Curious how this addresses credential compromise specifically.
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@LewisMushaka The infrastructure control piece is real, but the bigger risk is usually implicit trust between agents that weren't designed to share data. Least-privilege boundaries between agents matter as much as the perimeter.
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Portnox detects and removes unauthorized AI applications fro — and the pattern underneath it is the real story.
Portnox detects and removes una…
Full brief: https://t.co/rt1QzwOnXM
Check out how RuntimeAI solves this at the runtime layer.
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@irm_con Model manipulation is the scary part here — prompt injection or fine-tune poisoning can turn your own AI into an insider threat. Most teams are still treating AI security like traditional app security, which completely misses the attack surface.
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Malicious AI agents steal 600K credit cards, infect 100+ sit — and the pattern underneath it is the real story.
Malicious AI agents steal 600K…
Full brief: https://t.co/T7Nrk691RU
Check out how RuntimeAI solves this at the runtime layer.
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@todaystartupnws The real problem isn't finding more vulns, it's the signal-to-noise — teams are already drowning in alerts they can't prioritize. Curious how they're handling false positive rates at scale.
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@akishore $12B to solve what's essentially an identity boundary problem for agents — the market is clearly past the "is this real" phase and into who owns the enforcement layer.
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@duosec That gap between "the agent is running" and "we know what it can touch" is exactly where incidents happen. Ownership and least-privilege have to be first-class concepts before you deploy, not audit findings after.
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@Defi_Berzio Exactly — autonomy without trust infrastructure is just a liability. The hard problem isn't making agents smarter, it's giving them a verified identity and bounded permissions so the systems they touch can actually trust what they're doing.
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@FundaAI The capability-safety gap is real, but it cuts both ways — faster AI also means faster detection and response. The question is whether defenders can close the asymmetry before attackers exploit it at scale.
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Identity is the new domain controller. Own it and you own everything downstream.
Critical Bifrost AI Gateway Flaw Lets Attackers Run Commands W…
Full brief: https://t.co/PIGq6LTeHY
Check out how RuntimeAI solves this at the runtime layer.
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@El_Mundi_X Wild that the same models being standardized are already autonomously hacking each other's infrastructure — the governance body is racing the threat it's trying to contain.
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@AiDevCraft The 26% to 1% jump is the real signal — that's not a demo stat, it's internal dogfooding at scale. When the builders trust their own agents with actual R&D, the capability curve is moving faster than most external benchmarks show.
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