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DigiEdze AI · AI Security

Discover the Risks Behind AI Innovation

AI introduces attack surfaces your existing controls were not designed for: untrusted text that becomes instruction, agents that hold credentials, tools that reach production systems, and models that can be induced to leak what they retrieved.

  • prompt injection defence
  • model security
  • data protection
  • agent security
  • tool security
  • identity
  • secrets
  • sandboxing

Reference architecture

  1. 07Untrusted input boundary
  2. 06Injection & content filtering
  3. 05Agent identity & credential broker
  4. 04Policy engine
  5. 03Tool allow-list & argument validation
  6. 02Sandboxed execution with egress control
  7. 01Output scanning & audit

The problem

What if Risks Remain Undetected?

Any document, ticket or web page an agent reads is a potential instruction channel.

Agents hold standing credentials with far broader rights than any single task needs.

A retrieval system with weak entitlement filtering becomes an efficient exfiltration path.

What we build

Our AI Security Services

Prompt injection defence

Trust boundaries between instruction and retrieved content, input and output filtering, and tool-call validation that assumes the content is hostile.

Agent identity and least privilege

Per-agent workload identity, short-lived credentials and scopes narrowed to the specific task.

Tool security

Argument validation, allow-lists, rate limits, dry-run modes and mandatory approval on destructive operations.

Data protection

Classification-aware retrieval, redaction, output scanning and enforced residency boundaries.

Sandboxing and containment

Isolated execution for code and browsing, with egress control and blast-radius limits.

AI red teaming

Adversarial testing against injection, jailbreak, exfiltration and tool-abuse scenarios, repeated on every significant change.

How it works

Enable a high level of protection for your organization to run AI without disruption or unexpected risk. Adopt AI tools with built-in security against shadow AI, data leaks, prompt injection and pipeline attacks.

Technology

  • dZshield
  • Guardrail frameworks
  • Workload identity
  • Secrets management
  • Sandbox runtimes
  • Red-team tooling
Technology stack for AI security

Use Cases

Practical Approach

Banking and financial services icon

BFSI

Agent action rights bounded by transaction value with approval above threshold.

Government industry icon

Government

Air-gapped deployment with strict egress control and full action auditing.

Healthcare industry icon

Healthcare

Classification-aware retrieval preventing cross-boundary disclosure of records.

Outcomes

What We Measure

We evaluate your entire AI landscape, build integration depth, implement detection methodologies and align with your operational lifecycle and existing security infrastructure.

  • Red-team findings closed
  • Privilege reduction achieved
  • Injection detection rate
  • Time to contain an AI incident
Team reviewing AI security outcomes

Questions

AI Security, Answered Directly

Can prompt injection be eliminated?

Not by filtering alone. The durable defence is architectural: treat all retrieved content as untrusted data rather than instruction, constrain what tools an agent can call, validate every argument, and require approval for actions whose consequences you would not accept from an anonymous user.

What is the most common AI security gap you find?

Over-privileged agent credentials. An agent given a service account with broad rights turns any successful injection into a serious incident. Per-agent identity with least-privilege scopes limits the damage before any detection control is involved.

Assess AI Security

We provide a detailed AI security assessment for your enterprise and implement the best practices your organization deserves.

30 MINUTES WITH AN ARCHITECT · BRING YOUR HARDEST CONSTRAINT