AccuKnox AI Security Roadmap for Securing AI Models, Agents and Data (AI-SPM, AI-DR, AI-GRC, AI-DSPM) – 2026

The Complete AI-Security Platform Updates Coming Up in AccuKnox

AccuKnox announced AI Security 2.0, a framework for an identity-powered, zero-trust platform to secure AI models, Agents, and Data at RSAC 2026

It includes GPU-based prompt firewall optimisation, local unmanaged AI asset discovery (Ollama, vLLM, and MCP servers), cloud-native AI detection and response with auto-remediation, multi-cloud DSPM for AI training data and vector databases, AI-BOM with vulnerability scanning, and compliance frameworks (MITRE ATLAS, ISO 42001, and NIST AI RMF).

At RSA Conference 2026, AI security buzz was impossible to miss. The most in-demand product capability was AI red teaming. This roadmap reflects what enterprises are prioritising: runtime protection for agentic AI, visibility in shadow AI, and compliance-ready controls for regulated environments.

Production AI incidents in 2025-2026 exposed gaps in traditional security tooling. A hidden prompt in a PDF email activated an industrial pump via an AI agent with MCP access, causing equipment damage. An agentic CI/CD workflow with shell access was hijacked through prompt injection chained with cache poisoning. Radware demonstrated a zero-click exploit (ZombieAgent) that hijacks agents through hidden instructions without triggering traditional tools.

💡 Key Insight: The average AI breach costs $670K more than traditional breaches because AI systems have access to broader data sets, run with elevated privileges, and often bypass traditional security controls entirely.

AccuKnox AI-SPM is Deeply Integrated Where Your Models,  Agents, Workflow and Data Resides

See the AccuKnox AI Security platform in action: full asset discovery, prompt firewall, red teaming, and data classification across a multi-cloud heterogeneous stack.

Simon Willison’s “Lethal Trifecta”

Security researcher Simon Willison identifies three conditions that, when present together, make any agentic system exploitable by design: 

⚠️ If all three are present, the system is exploitable. 

Indirect prompt injection, where malicious instructions arrive through the data the model ingests, now succeeds with fewer attempts than direct attacks . 

The attacker never touches the prompt box. They poison a webpage, a PDF, an MCP tool description, or a memory entry and wait for the agent to ingest it.

Why These Roadmap Items?

AccuKnox prioritised these capabilities based on three inputs: production incident patterns and regulatory compliance timelines. The security outlook for AI is stark. Most cloud security tools treat models as stateless APIs and agents as web services. This architectural mismatch creates blind spots that attackers are actively exploiting. The table below maps the specific threats driving the roadmap to the features that address them.

The Full AccuKnox AI Security Offerings – What’s Live and What’s Coming Next

01. GPU-Based Prompt Firewall

The prompt firewall is being optimized for GPU-based execution to handle production traffic at scale. Key improvements include profiling model execution with flamegraphs to identify hotspots, changing core engine architecture to allow different models to execute on separate instances, supporting tenant-specific GPU instances, and comprehensive benchmarking across GPU types.

02. Local Unmanaged AI Assets Discovery

Shadow AI is running in 78% of enterprises with zero visibility. 

Local AI assets include developer-installed Ollama instances, MCP clients and servers, LangChain/LangGraph agents, Anthropic SDK usage, CrewAI frameworks, vLLM and NVIDIA Triton inference servers, vector databases, and locally deployed agents like OpenClaw. These assets are not registered in cloud accounts, do not appear in IAM logs, and operate outside traditional network monitoring.

03. AI Detection and Response (AI-DR)

Cloud-native detection and response for AI assets across AWS, Azure, and GCP. AI-DR correlates control plane API calls from CloudTrail, Azure Logs, and GCP Logs to detect unauthorized activity such as non-approved region deployments, public exposure of AI assets, and changes to sensitive data assets. Auto-remediation is handled via GitOps workflows and CDR (Continuous Detection and Response) policies.

04. Data Security Posture Management for AI (DSPM)

Training datasets, vector embeddings, and inference logs contain sensitive data that was never classified, never encrypted at rest, and in multi-cloud environments, sits in regions where it legally should not. 

Standard CSPM tools check infrastructure configuration but do not inspect data content. AI-DSPM scans AWS RDS (PgSQL, MySQL, MariaDB, Oracle, SQL Server), AWS Vector DB, Azure Blob Storage, Azure Object Storage, Azure SQL Database, GCP Cloud SQL, Google Cloud Storage, and Oracle Cloud Object Storage. 

Additional capabilities include log scanning for PII in application logs, integration with Microsoft Purview DLP, access control vulnerability assessment, data retention policies, and data residency compliance for India-specific regulations.

Additional DSPM capabilities:

• Log scanning for PII data in application logs with clear reports on internal data misuse
• Access control vulnerability assessment: MFA enforcement, storage encryption, key rotation, SCPs/RCPs/Cloud Armor
• Data retention policy implementation for legal and regulatory compliance
• Identify sensitive data lacking adequate protection (encryption at rest, TDE for Azure Blob Storage)
• Data residency compliance: CDR policy alerts if data asset created outside India region; remediation options

05. AI Bill of Materials (AI-BOM)

Models in production depend on frameworks with CVEs, datasets with licensing obligations, and inference servers with known vulnerabilities. 

None of this appears in traditional SBOMs. AI-BOMs are becoming mandatory in 2026 (Adversa AI). 

AccuKnox AI-BOM generates bills of materials based on discovered applications, detects vulnerabilities across the entire AI stack (model, framework, inference server), imports AI-BOMs from third parties to show licenses and component details, and compares multiple AI-BOMs to identify added, updated, or deleted components.

06. Intelligent Red Teaming

Manual red teaming is expensive, infrequent, and stale before ink dries. 

AccuKnox Intelligent Red Teaming dynamically generates adversarial probes based on the application domain, identifies the specific domain used by the agent and loads probes dynamically, and highlights the exact snippets in prompts or responses that violated policy clauses. 

Red teaming extensions include GCP Model Garden integration, CP Studio red teaming, and prompt firewall integration on AgentEngine behind GCP APIGEE AI Gateway.

AccuKnox AI Red Teaming is continuous running the same adversarial tests that a red team would run, at machine speed, every time the model or its configuration changes.

07. AI Application Inventory View

A centralized inventory view for all AI applications discovered across the environment. Categorized by application category, type, and sub-type. Includes export options for partial or full inventory data, and frontend navigation across categories. This gives security teams a single pane of glass for all AI assets in the organization.

08. AI-GRC and Compliance

AccuKnox is building compliance frameworks directly into the platform. 

Supported frameworks include MITRE ATLAS (adversarial threat landscape for AI systems), ISO 42001 (AI management system standard), and NIST AI Risk Management Framework. 

UI/UX improvements for compliance workflows make it easier to generate audit reports, track compliance posture, and remediate gaps.

09. AI Penetration Testing

AI Pen Testing provides automated adversarial testing across models, agents, and AI infrastructure. 

This includes model vulnerability testing to identify weaknesses in deployed models, AI attack simulation to replicate real-world adversarial scenarios, AI attack surface discovery to map all AI assets and their exposure points, and AI exploit simulation to test prompt injection, jailbreak, and sandbox bypass techniques in controlled environments.

Generally Available Today

The following AccuKnox AI Security capabilities are already production-ready and generally available.

KnoxClaw – Sandboxing for OpenClaw with Kernel-Level Protection (AI Agent Security)

While the Q2 2026 roadmap focuses on cloud-native AI security across models, agents, and data, KnoxClaw addresses a different but critical problem: securing AI agents that run with shell, filesystem, and network access on local infrastructure. OpenClaw reached 247,000 GitHub stars in under 60 days but accumulated 512 vulnerabilities in its first two months, including a CVSS 8.8 WebSocket authentication bypass.

KnoxClaw takes a different approach. Instead of patching vulnerabilities at the application layer, KnoxClaw enforces security at the Linux kernel syscall level using KubeArmor and eBPF.  

Read the full blog here: Introducing KnoxClaw – Secure your OpenClaw Instances with KubeArmor Sandboxing

FAQ