Intent security shifts AI security from checking what agents say to governing what they do. It evaluates purpose, context and behavior at runtime as agents access systems, trigger workflows and make decisions.
Read more →AI advantage will not come from simply using the latest model. It will come from the data, workflows, governance, infrastructure and operating capabilities built around AI that are harder to replicate.
Read more →Blanket restrictions are unlikely to eliminate Shadow AI. Enterprises need approved tools that are easy to use, safe paths for experimentation, and governance that scales with actual risk.
Read more →AI is making automated traffic a permanent part of digital infrastructure. The challenge is no longer just blocking bots, but governing which automation supports the business and which automation exploits it.
Read more →Encrypting an AI model’s reasoning does not automatically make it secure. Researchers found that encrypted reasoning traces could be replayed across sessions, users and models, creating risks around data leakage, model extraction and hidden prompt injection.
Read more →AI agents are already operating inside enterprise workflows, but governance, visibility, and runtime controls are not keeping pace. The real risk is no longer just who has access, but what autonomous systems can do once they have it.
Read more →Human Risk Management (HRM) is becoming essential in the era of agentic AI because cyber risk now comes from both people and the AI agents acting on their behalf. Training alone is no longer enough; organizations need visibility, context, and targeted action.
Read more →Most organizations are moving too fast on agent deployment and too slow on operating-model redesign.
Read more →The biggest shift is not more chatbots or AI assistants. It is enterprises moving toward multi-agent systems that can handle real operational work, not just fancy demos.
Read more →Companies can drive meaningful business impact by scaling incremental, manageable AI use cases instead of adopting a “big bang” approach. This means focusing on building capabilities, managing risk, and climbing a practical AI maturity curve.
Read more →Prompt injection is a cybersecurity threat unique to AI systems where attackers embed hidden instructions that cause generative models to behave in unintended ways, for example, potentially exposing data, executing unwanted actions, or bypassing safety guardrails. Enterprises deploying AI agents must treat this as a long-term security challenge with layered defenses.
Read more →Only a small group — just 13% of organizations, dubbed the “Pacesetters” — are truly ready. Most are racing ahead with ambition but lagging in infrastructure, governance, and measurement
Read more →Most enterprises are dangerously unprepared. There exists a critical readiness gap, with AI adoption far outpacing the enterprise safeguards needed to govern it.
Read more →Yes — through zero-click exploits like EchoLeak, which weaponize voice, context, and memory against your agents.
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