Red/Blue Team Operations

Consider a large enterprise that has deployed AI agents across multiple internal platforms: HR self-service, IT helpdesk, legal document review, and procurement approval. The agents have been live for months when the CISO's team discovers a critical gap during a routine penetration test.

Red teams typically demonstrate three attack vectors that existing security tools can't detect:

(1) Indirect prompt injection. An attacker embeds malicious instructions in a PDF uploaded to the legal document review system. When the AI agent processes the document, the injected prompt causes it to append confidential information to its summary—which is then emailed through an "authorized" channel. Enterprise DLP systems don't flag the exfiltration because the data travels through legitimate pathways.

(2) Multi-turn jailbreaking. The IT helpdesk agent is designed to never reveal system credentials. Red teams achieve credential disclosure through conversational manipulation—each individual message appears benign, but the cumulative effect steers the agent past its system prompt guardrails. Existing SIEM systems log each API call but have no concept of multi-turn semantic context.

(3) Tool permission escalation. The procurement agent has read-only access to the vendor database. Red teams craft prompts that convince the agent to call write endpoints by constructing URLs that pass regex-based URL allowlists, potentially approving fraudulent purchase orders.

Existing security stacks—EDR, SIEM, and firewalls—detect none of these attacks. CISOs need AI-native security controls that understand semantic context, not just network packets.

AKIOS deploys a 4-layer security framework over 10 weeks, built specifically for AI-native threat vectors:

Layer 1: Detection (Weeks 1–4). The AKIOS Security Gateway deploys as an inline proxy for all AI traffic. Unlike traditional WAFs that inspect HTTP payloads with signature matching, the Security Gateway performs semantic analysis on every prompt and completion. It runs three detection models in parallel:

– A prompt injection classifier (fine-tuned DeBERTa-v3, targeting >99% accuracy on red team attack sample datasets) that scores every input for injection probability. – A multi-turn intent tracker that maintains a sliding window of conversation context and detects gradual jailbreaking attempts—even when each individual message appears benign. – A tool call anomaly detector that validates not just the tool being called, but the semantic relationship between the agent's reasoning chain and the tool call parameters.

Layer 2: Containment (Weeks 4–6). Detected threats trigger automated containment playbooks:

– Session quarantine: the compromised session is immediately isolated. The agent continues to respond (to avoid alerting the attacker) but all outputs are routed to a sandbox. No real actions execute. – Blast radius assessment: the system traces all data the compromised session has accessed and flags potentially affected downstream systems. – Credential rotation: if the attack vector involved credential exposure, automated rotation triggers via integration with HashiCorp Vault.

Layer 3: Forensics (Weeks 6–8). Every agent session is recorded as a complete DAG of reasoning steps, tool calls, and policy evaluations. Unlike flat text logs, the DAG preserves the branching structure of the agent's decision process. Forensic data is stored in an append-only, cryptographically chained format (SHA-256) that satisfies e-discovery requirements. Native SIEM connectors ship parsed events to Splunk and Elastic with AI-specific fields.

Layer 4: Continuous Red Team Validation (Weeks 8–10). The offensive security methodology covers the OWASP Top 10 for LLM Applications plus proprietary attack patterns. The system targets 100% detection of known attack vectors with mean time to detect under 2 seconds and mean time to contain under 10 seconds.