Every program
SIEM and DetectionSecurity AutomationManaged ServicesZero TrustIdentity and AccessAI SecurityData ProtectionApplication SecurityCloud SecurityRisk and ComplianceStrategy and AdvisorySee all security programsthe team your security team calls.
people, agents and data
We set the rules for AI use and put in the controls that hold them.
eight domains, one program
sound familiar?
Teams are shipping generative AI before security has seen it. The attack surface grows while the usual frameworks wait for a rewrite.
Someone pastes source code, customer records or a forecast into a language model, and it now sits in a system your team does not control. Training ingestion, prompt logs and third party APIs each open a path that classic data loss prevention was not built to see.
Anyone who can type can try to override your instructions, pull out training data or steer the model. It is the easiest way into an AI system and the hardest to stop with a single control.
A tuned model holds real intellectual property. Carefully crafted queries can rebuild how it behaves without touching the weights, and adversarial inputs can quietly bend decisions in production.
The EU AI Act brings mandatory risk classification. The NIST AI Risk Management Framework shows up in more and more security reviews. ISO 42001 gives AI management its first certifiable standard. Three frameworks, one set of evidence to keep.
each one mapped
Every domain lines up with a requirement in the NIST AI RMF, the EU AI Act or the OWASP LLM Top 10, so the work you do for one helps with the others.
01AI Governance and Risk Management
Set the risk framework, name the owners and give the board a report it can read. Aligned to NIST AI RMF Govern and EU AI Act Article 9, with AI risk folded into the enterprise risk program you already run.
02Data Protection and Governance
Classify and protect training data, inference inputs and model outputs with DLP policies that understand AI. Data quality, provenance and consent follow the EU AI Act Article 10 data governance requirements.
03Prompt Security and Output Integrity
Layered input validation and output filtering against prompt injection, jailbreaks, system prompt leakage and data exfiltration. Covers OWASP LLM01, LLM05 and LLM07.
04Model Integrity and Robustness
Protect model weights, check inference accuracy and harden serving infrastructure against adversarial attacks and data poisoning. Aligned to EU AI Act Article 15 on accuracy, robustness and cybersecurity.
05AI Supply Chain Security
Vet third party models, review AI vendor practices and secure how models are procured. Covers OWASP LLM03 and the NIST AI RMF Map function for third party risk.
06Monitoring, Detection, and Response
Spot odd model behavior, find shadow AI and keep incident playbooks written for AI threats. Supports the NIST AI RMF Manage function and EU AI Act Article 12 record keeping.
07Transparency and Human Oversight
Systems that explain their decisions, keep a person in the loop and push back on misinformation. Addresses EU AI Act Articles 13 and 14 and OWASP LLM09.
08Compliance and Regulatory Mapping
Map controls across NIST AI RMF, ISO 42001 and the EU AI Act once, so audits draw on the same evidence. Technical documentation and records follow EU AI Act Article 11.
start where you are
Nobody starts at the top. Pick the level that sounds like your Monday morning and we plan the climb from there.
AI security is on the radar but nobody owns it. Teams adopt tools without a review and shadow AI is everywhere. First job: map what exists.
AI Jumpstart
Policies are written, owners are named and AI use goes through an approval process. Red team exercises test the controls and show the gaps.
Govern and Test
Controls enforce the policy on their own. Monitoring flags odd model behavior and data exposure, and compliance reporting runs all the time instead of on request.
AI Security Posture Management
AI security runs as a repeatable program. Detection and response are automated, policies adapt to each new model deployment and the program scales across business units.
Managed AI SecOps
pick your door
Three common ways into an AI security program.
first
Employees use dozens of AI tools that IT has never heard of. We start with discovery, then build the policies and controls that bring unsanctioned use into something managed and secure.
second
When a team builds customer facing AI features, security belongs in the architecture from day one. That means prompt injection defenses, output filtering, rate limiting and abuse detection.
third
The EU AI Act is coming into force and NIST AI RMF keeps turning up in security reviews. One compliance plan can cover all three frameworks at once.
Tell us where AI is already in use and where it is heading. We will show you where to spend first.
Mapped to NIST AI RMF, ISO 42001 and the EU AI Act.