Niksun

As enterprises continue integrating generative AI into customer service, software development, internal knowledge management, and business automation, the attack surface surrounding large language models (LLMs) continues to grow. One of the fastest-emerging threats is prompt injection—a technique that manipulates AI models into ignoring intended instructions, exposing sensitive information, or performing unauthorized actions. While application-level safeguards remain essential, they cannot always detect every malicious prompt or identify how attackers interact with AI systems over time. This is where network telemetry  becomes an important layer of defense. By continuously monitoring communications between users, AI applications, APIs, and supporting infrastructure, organizations gain valuable visibility into suspicious activity that may indicate prompt injection attempts. Understanding Prompt Injection Attacks Prompt injection occurs when an attacker crafts malicious input designe...

As organizations continue moving critical workloads to the cloud, protecting sensitive information has become more challenging than ever. While many security strategies focus on preventing unauthorized access, attackers often achieve their objectives by quietly moving data out of the environment. Unauthorized outbound traffic can indicate compromised workloads, insider threats, misconfigured applications, or active data exfiltration attempts. Without continuous visibility into cloud egress traffic, these activities can remain undetected until significant damage has already occurred. Continuous cloud egress monitoring, supported by advanced network analytics , enables security teams to identify suspicious outbound communications in real time, helping reduce the risk of unauthorized data transfers and strengthen enterprise security. Why Cloud Egress Traffic Matters Cloud egress traffic refers to data leaving cloud-hosted applications, virtual machines, containers, storage services, or ot...

Authentication has become one of the most heavily protected aspects of enterprise security. Organizations invest in multi-factor authentication (MFA), single sign-on (SSO), identity providers, and zero-trust initiatives to verify users before granting access. While these technologies significantly strengthen access control, they are not immune to abuse. Today's attackers are increasingly focusing on what happens after authentication. Instead of attempting to break passwords or bypass MFA directly, they exploit gaps in authentication workflows, misuse valid session tokens, and hijack authenticated sessions to move through enterprise environments unnoticed. Because these activities often involve legitimate credentials, they may never generate the alerts security teams expect. Understanding authentication gap exploitation has become essential for organizations seeking to defend against modern identity-based attacks . Why Authentication Is No Longer the End of the Story Traditional sec...

Artificial intelligence has fundamentally changed cybersecurity , but not just for defenders. The same technology helping security teams analyze billions of network events is also giving attackers the ability to automate reconnaissance, generate convincing phishing campaigns, evade detection, and continuously refine their tactics. This has created a new reality where cybersecurity is no longer a contest between humans and hackers. Increasingly, it is AI defending against AI. For enterprises, success depends on adopting behavioral AI cybersecurity detection that can recognize intent and abnormal activity rather than waiting for known attack signatures. The Rules Have Changed Traditional cyberattacks  followed relatively predictable patterns. Malware families were cataloged, indicators of compromise were shared, and signature updates helped organizations block known threats. AI-powered attacks don't play by those rules. Modern attacker AI can observe how defensive tools react, modify...