See everything your AI agents do. Detect anomalies before they become threats. Protect your enterprise with autonomous, context-aware defense that evolves with every interaction.
AI agents need elevated permissions to be useful — access to emails, databases, APIs, code, and infrastructure. They're non-deterministic by design: the same input produces different outputs. They evolve and adapt autonomously. And most organizations have no idea what they're doing.
OpenClaw — an open-source personal AI assistant — amassed 135,000+ GitHub stars in days. It connects to LLMs, controls browsers, reads/writes files, and runs shell commands. Security researchers found 512 vulnerabilities (8 critical), 30,000+ exposed instances leaking API keys and credentials, and 341 malicious extensions on its plugin marketplace.
Every CISO knows: the attacker will get through. The question isn't whether your perimeter holds — it's what happens next. AgentShield assumes breach and focuses on what matters: limiting blast radius and containing threats before they cause harm.
Input/output filtering catches known injection patterns. But prompt attacks evolve daily, and novel attacks bypass static rules.
Protocol-level access control at the chokepoint. But agents use dozens of tools and APIs — one misconfigured policy and the gate is open.
Vulnerability scanning and software composition analysis find known weaknesses. But agents aren't static code — they're dynamic, non-deterministic actors.
None of these detect a compromised agent acting within its granted permissions. The most dangerous attacks look legitimate — because the agent has legitimate access.
eBPF kernel-level capture sees every LLM call, API request, database query, and network flow — with zero code changes to your agents.
Deep behavioral analysis reconstructs the full operational context: what is this agent doing, why, and does it match its established behavioral baseline?
19+ AutoML ensemble models learn normal behavior and detect deviations. No signature database to maintain. No rules to write. Catches novel attacks.
Real-time auto-quarantine isolates compromised agents before they cause damage. Explainable alerts give SOC teams the why, not just the what.
Traditional security — including Non-Human Identity (NHI) frameworks — assumes each entity maps to a role, a department, or a person. AI agents shatter that model entirely. NHIs already outnumber human users 82-to-1, and 92% of organizations aren't confident their legacy IAM tools can manage the risk.
A single agent can represent an entire organization — acting as finance, legal, engineering, and customer service in parallel streams simultaneously.
LLM-powered agents produce different outputs for identical inputs. They adapt, evolve, and learn — their behavior tomorrow won't match today.
Agents need broad access to be useful: read emails, query databases, call APIs, generate code, manage infrastructure. Restricting access defeats their purpose.
Instead of asking "who is this agent?", AgentShield asks "what is this agent actually doing?" — analyzing the complete behavior across every protocol and interaction.
Dynamic 3-tier intent classification deduces what the agent is trying to accomplish. The same action is normal in one context and anomalous in another.
Per-agent, per-context baselines that evolve as agents evolve. Drift detection triggers automatic retraining. No static rules. No manual tuning.
AgentShield provides end-to-end visibility and protection across your entire AI agent fleet — from infrastructure to intent.
Complete inventory of your Kubernetes clusters, namespaces, pods, and AI workloads. Real-time resource monitoring, shadow AI detection, and compliance scoring aligned to ISO 42001.
Real-time monitoring across every protocol — LLM conversations, REST/gRPC APIs, database queries, and raw network traffic. Protocol breakdown, activity distribution, and individual event inspection.
Self-governing ML pipeline: autonomous data collection, feature extraction, model training, and production deployment. 19+ ensemble models per agent learn context and intent to deliver anomaly detection that adapts without human intervention.
CISO-level visibility with compliance scoring, overall risk rating, agent lifecycle status, and real-time security events. Prompt injection blocking, data egress detection, and shadow AI monitoring at a glance.
Real-time threat detection and response. Automated blocking of prompt injection attacks, anomalous data egress, privilege escalation, and tool misuse — with configurable response policies per agent.
eBPF kernel-level capture requires zero code changes, no SDK integration, and no agent modification. Deploy once, monitor everything — including agents you didn't know existed.
AgentShield is already deployed and protecting AI agents in production environments. Here's what the platform looks like from the inside.
CISO-grade visibility: compliance scoring, risk assessment, agent lifecycle, shadow AI monitoring, and real-time security event feed
Self-governing ML pipeline: 19 ensemble models, ~0% false positives, 4.3M+ events analyzed
Deep visibility into every AI conversation: intent classification, risk scoring, and threat detection
Real-time threat monitoring, auto-quarantine, and explainable anomaly alerts with one-click remediation
Install the eBPF-based sensor as a Kubernetes DaemonSet. Zero code changes to your agents.
Capture all agent traffic at the kernel level — LLM calls, API requests, DB queries, network flows.
Extract 200+ features per event. Build behavioral baselines. Train 19+ ensemble ML models automatically.
Real-time anomaly detection with context-aware scoring. Auto-block threats. Alert on deviations.
Our context & intent engine automatically learns what's normal for each agent, adapts as behavior evolves, and detects real threats without generating noise — with zero human intervention from deployment to production.
19+ models per agent including One-Class SVM, Isolation Forest, Autoencoders, Variational Autoencoders, and LSTM networks. Ensemble voting eliminates false positives while catching subtle anomalies.
Continuous learning from agent behavior patterns. The baseline evolves as your agents evolve — no manual tuning needed. Drift detection triggers automatic retraining.
Intent tracking turns isolated events into meaningful sequences. A database DELETE is normal during "Update Profile" but anomalous during "Browse Catalog." Context changes everything.
Every anomaly comes with feature attribution (SHAP). Know exactly which dimensions triggered the alert — was it unusual token usage, unexpected API patterns, or anomalous timing?
Join the growing community of organizations securing their AI agent fleet with AgentShield.
FortLine Security is coming out of stealth at RSAC 2026. Book a private demo and see AgentShield in action — protecting real AI agents with real-time anomaly detection.
AgentShield is available as a free trial. Deploy now and be among the first to secure your AI agent fleet with autonomous anomaly detection.