38 Companies Breached. 331M+ Records Stolen.

CVEs (2)

CVE-2026-74232China-Made ZBT Routers Ship Wi…

CVE-2026-74233China-Made ZBT Routers Ship Wi…

RCE (5)

RCEThreatsDayAug 27

RCEn8n AI Workflow Automation Pla…Aug 21

RCEResearchers Combined AI Assist…Aug 14

RCECISAAug 14

RCEnginxAug 14

Vendors (2) — click to see breach

Perimeter Categories

EDR 3CDN 1

“You can’t govern what you can’t see.”

Continuously scans cloud, IDE, endpoint, and network to inventory every AI agent — registered or rogue. Classifies and risk-scores shadow AI automatically. One-click to import into governance.

⚠️ The gap it fills Wiz and Orca scan cloud misconfiguration. They don’t discover AI agents installed by developers or injected via compromised vendors. Your unknown agents are your biggest risk.

• Cloud scanner (AWS/Azure/GCP Lambda, Bedrock, SageMaker)
• IDE scanner (VS Code, Cursor, MCP servers)
• Endpoint scanner on developer laptops
• Shadow AI Inbox with auto-severity classification
• One-click shadow AI → governed agent pipeline

“No credential. No access. No breach.”

Provisions every AI agent with a SPIFFE/X.509 cryptographic identity. Short-lived certs, auto-rotating. TPM 2.0 hardware attestation. Agent DNS blocks unknown agents at the network layer.

⚠️ The gap it fills Legacy identity providers were built for human identity. They have no concept of non-human agents operating at machine speed with no user present to respond to an MFA prompt.

• SPIFFE X.509 SVID with RSA-2048, auto-rotating
• TPM 2.0 hardware attestation + PCR drift detection
• Zero-touch bootstrap for new agents
• Agent DNS: NXDOMAIN for unknown agents
• Blueprint-based permission inheritance

“Stop it before it executes. Not after.”

OPA/Rego policy engine with sub-1ms evaluation and fail-closed enforcement. Natural language to Rego compiler. Merkle-chain audit proves policies were never tampered with.

⚠️ The gap it fills Traditional SIEMs alert on what already happened — 73 days after the breach in the average case. The AI Control Plane enforces policy before the action executes, not after the damage is done.

• OPA/Rego engine, sub-1ms, fail-closed
• NL-to-Rego compiler: write policy in plain English
• Merkle-chain tamper-evident audit trail
• Multi-tenant RBAC + Separation of Duties
• Cross-site policy cascade for distributed fleets

“Inspect every token in, every token out.”

Bidirectional DLP scanning at <5ms latency. Prompt injection detected and stripped on input. PII, PHI, credentials caught on output. Behavioral risk score (0–100) triggers auto-suspend.

⚠️ The gap it fills Traditional NGFWs and CASBs see LLM traffic as an encrypted blob. They cannot inspect prompts, detect injection inside a conversation, or catch data leaking in an AI response.

• Bidirectional DLP: input (prompt injection) + output (data leakage)
• ML behavioral baselines per agent with adaptive thresholds
• Risk score 0–100 triggers auto-suspend or rate-limit
• No-code guardrail builder for business users
• Data Proxy: field-level masking before agent sees data

“Every tool call. Governed.”

Multi-tenant governed gateway for all agent-to-tool communication. 500+ pre-built integrations. 3-level kill switch (agent / tool / platform-wide) propagating in <50ms. OWASP MCP03 sanitization on every call.

⚠️ The gap it fills No existing vendor governs at the MCP protocol layer. Raw MCP deployments have zero security, zero multi-tenancy, and zero audit trail. This is the fastest-growing unguarded attack surface in enterprise AI.

• 3-level kill switch: per-agent, per-tool, platform-wide — all <50ms
• 500+ pre-built integrations with auto-discovery
• BYOM overlay: wrap existing MCPs without code changes
• Circuit breaker + health monitoring per connection
• Full OWASP MCP03 input/output sanitization + DLP

“Catch drift before it becomes a breach.”

30-day rolling behavioral baselines per agent across frequency, pattern, volume, and temporal dimensions. LSTM sequence modeler detects multi-step attack chains. HRIS integration auto-suspends agents when their owner is terminated.

⚠️ The gap it fills Signature-based EDR detects known malware signatures for human endpoints. It has no baseline for an AI agent that begins exfiltrating data through an API it was legitimately authorized to call.

• Rolling 30-day baseline: frequency, pattern, volume, temporal
• LSTM sequence modeler for multi-step attack patterns
• Composite risk score from 6 signals
• HRIS integration: auto-suspend on employee termination (<30s)
• Adaptive OPA thresholds by agent role + risk profile

“Stop any AI agent, anywhere, in under 50ms.”

Three graduated kill levels: per-agent, per-tool, platform-wide. All propagate via NATS JetStream in <50ms. Captures last 100 actions as forensic state. Quarantine mode preserves evidence for investigation.

⚠️ The gap it fills No competitor offers this. When an AI agent goes rogue — or when a breach is detected — you need a hard stop. Every second it keeps running is more data exfiltrated, more damage compounding.

• L1/L2/L3 kill: per-agent, per-tool, platform — all <50ms via NATS
• Forensic state capture: last 100 actions, memory snapshot, credentials
• Quarantine mode: isolate for investigation, preserve evidence
• Escalation chains: auto-response → SOC alert → human required → kill
• Reprieve mechanism: 24-hour lease for controlled investigation post-kill

“Autonomous incident response. AI-native.”

Five-phase automated playbook: DETECT → QUARANTINE → INVESTIGATE → REMEDIATE → VERIFY. Auto-classifies incidents (true positive / false positive / inconclusive). Blast radius containment automatically quarantines agents that interacted with compromised agent.

⚠️ The gap it fills Traditional SOAR platforms orchestrate conventional security events. They cannot terminate an AI agent, rotate its credentials, update its behavioral model, or quarantine the agents it spoke with — because they were built before AI agents existed.

• 5-phase automated playbook from detection to verification
• Auto-classification against 200+ known AI attack patterns
• Blast radius containment: quarantine all interacting agents
• True positive: terminate + revoke + rotate + update models
• False positive feedback loop continuously improves detection

“Mission control for autonomous AI operations.”

65+ page operational dashboard. Access review campaigns. ‘The Reaper’ auto-decommissions agents when their human owner is terminated. Vault Broker injects credentials with 5-minute TTL — never stored in agent memory.

⚠️ The gap it fills ServiceNow manages human IT requests on ticket-based cycles. AI agents are deployed, modified, and compromised in minutes. You need lifecycle governance that operates at agent speed, not ticket speed.

• Access review campaigns with auto-apply decisions
• ‘The Reaper’: HRIS webhook auto-revokes terminated employees’ agents (<30s)
• Vault Broker: just-in-time 5-min TTL credential injection, never persisted
• Per-tenant budget caps with 4-tier alerts (50/75/90/100%)
• Unified health, credential lifecycle, budget, SLA dashboard

“Route every LLM call to the right model, at the right cost, with automatic failover.”

Unified API routing LLM requests to the optimal provider based on cost, latency, and compliance. Semantic caching reduces redundant calls 15–30%. Automatic failover in <50ms. Budget enforcement with hard limits.

⚠️ The gap it fills Portkey does basic routing at $30K/year. It has no DLP scanning, no compliance-based routing (data residency), no semantic caching, and no budget enforcement. It routes traffic — it doesn’t govern it.

• Multi-provider routing: OpenAI, Anthropic, Bedrock, Azure, GCP, custom
• Cost/latency/compliance-based routing policies
• Automatic failover <50ms; per-provider circuit breaker
• Semantic caching: 0.80–0.95 similarity threshold, 15–30% cache hits
• Budget enforcement per-agent + cost anomaly detection

“Model registry, feature store, edge inference — one platform.”

Formal model lifecycle (draft/staging/production/archived). Feature Store with online (<5ms) and offline serving. Hybrid scoring engine routes inference between edge (<1ms quantized) and cloud. Drift-triggered auto-retraining.

⚠️ The gap it fills MLflow + Feast + custom inference each solve one piece. ML Intelligence Hub is the piece nobody built: unified lifecycle + edge inference routing + drift-triggered retraining + 7-dimension cost attribution — all integrated.

• Model Registry: versioning, rollback, lineage, lifecycle management
• Hybrid Scoring: edge <1ms quantized vs cloud full-precision auto-routing
• Feature Store: online <5ms + offline point-in-time with freshness monitoring
• Drift Engine integration: auto-retraining on data/concept drift
• 7-dimension cost attribution: agent/model/team/customer/feature/time/provider

“A cost spike is a security signal. Treat it like one.”

7-dimension real-time cost attribution: agent, provider, model, team, customer, feature, time. Wasm token counter in-proxy at 50–100 microseconds. Budget hard limits stop agents before they overspend. Runaway agent detection.

⚠️ The gap it fills Kubecost knows GPU-hours. AI Cost Intelligence knows “Agent-47 spent $142 on Claude Sonnet for fraud detection on Tuesday.” Runaway cost is runaway behavior — and only one product treats them as the same signal.

• Wasm token counter in proxy: 50–100 microsecond overhead
• Live model pricing catalog: 200+ models, 15+ providers, updated every 15min
• Budget hard limits: block requests when agent exhausts budget
• Cost anomaly detection: ML-based spending spike alerts
• Chargeback engine: per-customer invoices + per-team internal allocation

“Audit evidence as a byproduct of governance.”

Continuous compliance across 13+ frameworks — SOC 2, FedRAMP, ISO 27001/42001, EU AI Act, HIPAA, PCI-DSS, NIST AI RMF. Evidence auto-generated from RuntimeAI telemetry. Open Audit Marketplace connects enterprises with certified audit firms.

⚠️ The gap it fills Vanta collects attestations from cloud infrastructure. It has no understanding of AI agent behavior, no EU AI Act or ISO 42001 mappings, and no way to generate evidence from an AI governance layer — because none of its customers had one.

• 13+ frameworks: SOC 2, FedRAMP, ISO 27001/42001, EU AI Act, HIPAA, PCI-DSS, NIST AI RMF
• Evidence auto-generated from platform telemetry (audit trails, Merkle chain, access reviews)
• Gap tracking with SLA-based remediation assignment
• Audit Marketplace: open to any qualified audit firm; time-limited scoped access
• Blockchain-anchored compliance certificates with tamper-evidence verification

“Only certified agents enter your environment.”

Three-sided platform: Builders publish, Enterprises deploy, Trust layer certifies. AAIC certification includes third-party behavioral audit. Risk scoring weights permission scope, data access, integration breadth, update frequency, and builder reputation.

⚠️ The gap it fills Your developers are installing AI agents from GitHub, npm, and PyPI with no security review. The AI agent supply chain attack surface is the same as software supply chain — and it’s moving five times faster.

• 6-step publishing wizard with compliance gating
• AAIC certification: third-party behavioral audit by registered firms
• Risk scoring: permission scope (30%), data access (25%), integration (20%), frequency (10%), reputation (10%)
• Shadow AI Import: discover unmanaged agents and bring into governance
• Stripe Connect billing: free/per-seat/per-action/outcome-based; 20% platform fee

“Every non-human identity — issued, governed, and revoked with the same rigor as human identity.”

Centralized governance for every non-human identity: service accounts, API keys, OAuth tokens, machine certs, cloud IAM roles, AI agents. Bot-CA issues short-lived X.509 SPIFFE certs. O(1) hash-based revocation — not cascading policy lookups.

⚠️ The gap it fills Oasis Security ($190K/year) solves NHI credentialing but not for AI agents specifically. It lacks TPM hardware attestation, has no AI-agent behavioral monitoring, and doesn’t integrate with agent governance platforms.

• Centralized NHI Registry: auto-discovery across AWS/Azure/GCP/on-prem
• Credential posture: rotation schedules, expiry, over-privilege, unused credential detection
• NHI Drift Detection: per-NHI behavioral baseline + scope creep detection
• Bot-CA: short-lived X.509 certs (1–24hr TTL), auto-rotating, instant OCSP revocation
• O(1) hash-based revocation: per-NHI, per-tenant, or global — no cascading policy lookup

“Valid credential. Wrong behavior. Caught.”

Two-layer defense: Identity Fraud Shield models valid-credential-wrong-behavior (the hallmark of compromised AI credentials). Activity Fraud Shield detects multi-step attack sequences within authenticated sessions. Both integrate directly with Kill Switch for automatic response.

⚠️ The gap it fills Generic UEBA tools applied to AI agents generate massive false-positive rates because they were trained on human behavior. Fraud Shields are AI-native: LSTM sequence modeling of API chains, not user session patterns.

• Per-agent behavioral baseline: frequency, resource access, API sequences, timing
• Real-time deviation scoring against baseline (0–100)
• LSTM sequence modeler: multi-step attack chain detection (recon→escalation→exfil)
• Session-level anomaly: full context analysis, not individual events
• Kill Switch integration: auto-suspension on high-confidence fraud with forensic package

“Control what your agents remember — and what they forget.”

Governs agent memory as a first-class security object. Policy-based filtering of sensitive content at write time. Memory poisoning attack detection. TTL-based automatic purge with audit trail. GDPR right-to-erasure support.

⚠️ The gap it fills No vendor addresses agent memory governance. AI agent memories accumulate without access controls, expiry policies, or audit trails. A memory poisoning attack can corrupt an agent’s behavior without touching a single API key.

• Policy-based memory write filtering (PII, PHI, secrets blocked at write)
• PII Shield integration: redact/block before persistence
• Memory expiry + TTL: auto-purge with full audit trail
• Memory poisoning prevention: adversarial injection detection
• Retrieval authorization: every memory read policy-enforced and logged

“Give AI agents a wallet — with guardrails.”

Financial infrastructure for agent-initiated transactions. Per-agent virtual cards with spend limits. Vendor registry (agents can only transact with allowlisted merchants). Approval gates for high-value transactions. Every transaction in immutable ledger.

⚠️ The gap it fills No financial controls exist for AI agents today. Agents authorized to make purchases can spend without limit, with any vendor, at any time. One prompt injection or runaway loop away from significant financial exposure.

• Agent virtual cards: per-agent card numbers + CVVs, hard spend limits
• Vendor registry: allowlisted merchants only, no ad-hoc transactions
• Approval gates: high-value + out-of-policy → human approval before execution
• Per-agent, per-transaction, per-vendor, per-period limits
• Agent-to-agent settlement ledger: feeds into FinOps dashboards

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