Agentic Al Security
This consultancy work package is designed to address the unique challenges of Agentic AI—systems that don't just "chat" but autonomously plan, use tools, and access data across your enterprise.
Traditional Data Security Posture Management (DSPM) focuses on where data is; this package evolves that to focus on how autonomous agents interact with that data.
In a standard environment, data risk is human-centric. In an Agentic environment, the risk shifts to:
Reasoning Paths: How an agent decides to access data.
Tool Use: The permissions granted to agents to read/write to databases.
Memory & State: What the agent "remembers" about sensitive sessions.
Objective: Map the "Shadow AI" landscape and identify every autonomous agent with data access.
Agent Profiling: Categorize agents by "Agency Level" (e.g., Read-only vs. Full Action agency).
Data Lineage Mapping: Identify which LLMs and agents are touching Sensitive, PII, or Proprietary data.
Tool & Plugin Audit: Inventory all connectors (APIs, Database drivers, ERP plugins) used by agents.
Deliverable: Agentic Data Flow Map & Risk Heatmap.
Objective: Evaluate the configuration and technical safeguards of the identified agents.
Area Focus
Identity & Auth Does the agent use a unique machine identity? Is it using "Least Privilege" for its API calls?
Prompt Injection Can an external input trick the agent into exfiltrating database schema or records?
Memory Security Is the agent’s "short-term memory" (context window) or "long-term memory" (vector DB) encrypted?
Boundary Control Are there "hard rails" preventing the agent from crossing from a Public zone to a Corp zone?
Objective: Establish the rules of engagement for autonomous actors.
Policy Definition: Create an "Agentic AI Usage Policy" detailing what data types are "off-limits" for autonomous processing.
Human-in-the-Loop (HITL) Triggers: Define high-risk actions (e.g., "Deleting a record," "Sending an external email") that require human approval.
Model Context Protocol (MCP) Implementation: Standardize how agents securely request data from host applications to prevent credential leakage.
Objective: Shift from static posture to active defense.
Behavioral Baselining: Establish "normal" data access patterns for agents.
Anomaly Detection: Set alerts for "Goal Drift" (when an agent starts performing tasks outside its original prompt instructions).
The "Kill Switch": Design an emergency revocation protocol to instantly strip an agent of its data access if a breach is detected.
Note: We recommend integrating with specialized platforms like Zenity, Netskope, or Microsoft Purview for AI to automate this continuous monitoring.
Agentic Security Strategy: A 12-month roadmap for securing autonomous workflows.
Configuration Hardening Guides: For platforms like OpenAI Assistants, LangChain, or Microsoft Copilot Studio.
Data Protection Impact Assessment (DPIA): Specific to AI-automated data processing.
Red Teaming Report: Results from simulated "Agent Hijacking" and "Data Poisoning" attacks.
For more information on the Work Packages you can contact us in any of the following ways quoting the Work Package ID
Schedule an Appointment for more information
Contact us on info@techstrategygroup.org
Complete our Enquiry form