Most CISOs can't see their AI agents. Attackers can.
In Okta's Global CISO Insights 2026, only 47% of security leaders were confident they could identify every AI agent in their environment. We inventory them, model what they can touch, break them the way an attacker would, and give you evidence that holds up with your board.
From one system to the whole program.
AI Exposure Sprint
One LLM application or agent. Threat model, adversarial test, executive brief, 90-day roadmap, retest option.
- Prompt injection, direct and indirect
- Tool and function abuse; MCP server permissions
- Data leakage through RAG and memory
- Agent identity, privilege and audit-trail review
Adversarial AI Assessment
Multi-system and agentic: multi-agent chains, RAG pipelines, MCP attack paths, and the social-engineering routes AI opens against your staff.
- Findings mapped to the OWASP Top 10 for LLM Applications (2026), the OWASP Top 10 for Agentic Applications, and MITRE ATLAS
- 30-day retest included
- A methodology document for your auditors
One assessment runs at a time, so you'll know the start date before you sign.
AI Governance Baseline
NIST AI RMF and ISO/IEC 42001 readiness: inventory, risk register, policy stack, board memo, and a gap list ready for a certification body.
- AI committee charter and decision rights
- Third-party AI due-diligence questionnaire
- Internal-audit support before certification
RedOps prepares you; an accredited certification body issues the certificate.
Specific enough to fix. Written for the board to read.
Every finding names the attack path, the business impact, the regulatory consequence, the fix, and an owner with a date. Then we retest.
Indirect prompt injection drives a data export
- Mapped to
- OWASP LLM01 Prompt Injection · OWASP ASI02 Tool Misuse · MITRE ATLAS AML.T0051
- Path
- Support agent reads an inbound ticket → the ticket body carries an instruction → the agent calls
export_contacts→ the file is posted to an attacker-controlled URL - Impact
- Bulk disclosure of customer personal data; a notifiable event under NYDFS §500.17
- Fix
- Restrict
export_contactsto human-approved runs; strip instructions from untrusted fields; egress allowlist on the agent's network identity - Owner / date
- Named owner and due date, with a scheduled retest
Tested, not theorized.
AI systems and agents inventoried or tested.
engagements delivered for regulated firms and their vendors.
As of October 2026
From AI risk to board approval.
“Putting generative AI into our underwriting pipeline opened new attack surfaces: prompt injection, data poisoning, model drift. RedOps built our model inventory and risk register and mapped our controls to the NIST AI RMF. Our board got the clarity it needed to approve AI at scale.”
“Enterprise buyers were stalling our pipeline with AI risk questionnaires on top of SOC 2 requests. RedOps designed our AI acceptable-use framework and governance model quickly. We cleared every vendor risk assessment and cut months off our enterprise sales cycle.”
Clients are identified by role and sector to protect their confidentiality.
Get a straight answer in 20 minutes.
Tell us what's in production: copilots, agents, RAG, MCP servers, or a board asking whether any of it can be hijacked. You'll leave the call with a recommended scope, and a written proposal follows within two business days.