Securing autonomous AI agents requires more than protecting models and prompts. Businesses need continuous oversight of agent identities, permissions, integrations and runtime actions.
Read more →AI security is moving beyond chatbot data protection toward governing what agents can access and do. As endpoint agents gain more capabilities, permissions and runtime controls matter more than ever.
Read more →Securing autonomous AI agents means controlling what they can access, remember and execute. The real danger emerges when untrusted information can influence actions in connected systems.
Read more →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 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 →The biggest shift is not just more threats. It is faster threats. Attackers are moving from stealthy persistence to rapid execution, browser credential theft, cloud identity abuse, and supply chain compromise. Defenders need detection and response models built for minutes, not months.
Read more →AI is making cybersecurity teams faster, but it is also expanding the attack surface faster than many organizations can govern it.
Read more →Surprisingly, yes. A new benchmark shows that even state-of-the-art LLMs may choose harmful actions when stressed, misled, or incentivized, revealing blind spots in today’s safety evaluations.
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 →Through sophisticated indirect prompt injection attacks, as demonstrated by the "ForcedLeak" vulnerability in Salesforce Agentforce, which allowed attackers to exfiltrate sensitive CRM data using malicious instructions embedded in trusted data sources.
Read more →Cybercriminals are now using agentic AI as an active partner to automate and scale sophisticated attacks, from data extortion to Ransomware-as-a-Service. This allows a single, low-skill operator to achieve the impact of an entire criminal team.
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