Agentic AI · Deployment & Security

Deploy AI agents — without deploying new risk.

AI agents are the next attack surface. The same teams who run your network, secure your endpoints, and watch your SOC 24/7 are bringing that infrastructure and cybersecurity discipline to agentic systems — the same identity model, the same audit trail, the same monitoring posture, extended to a new layer. So the agents running your business can't be turned against it.

What's at stake

Every new layer is a new attack surface.

Laptops, cloud, mobile, IoT, OT — we've helped clients secure each wave. Agentic AI is the next one. The controls translate from what we already do, but the agent layer needs new shape. Three categories of risk that did not meaningfully exist 18 months ago:

01

Agents hold credentials.

Every agent has API keys, OAuth tokens, or service-account credentials. A prompt-injection attack on one agent is now a credential compromise across every system that agent touches.

02

Agents take actions.

An LLM that writes is one thing. An agent that moves money, sends email on your behalf, edits production code, or terminates customer accounts is a different class of risk. A single misconfigured prompt becomes a real-world incident.

03

Agents are audited differently.

Your SIEM doesn't speak natural language. Logs from agent reasoning, tool selection, and prompt decisions need different shape, retention, and review than traditional application logs — or you'll learn what your agent did weeks after it did it.

What we deliver

A secure-by-default way to ship agents.

Built on the same SOC, identity stack, and compliance discipline we already operate for our MSP and MSSP customers. From design through production to ongoing monitoring — we work alongside your team or own deployment end-to-end.

Identity & access for agents

Treat every agent as a non-human employee in your IdP — Okta, Microsoft Entra ID, or Auth0. Unique identity per agent, OAuth 2.0 / OIDC flows, short-lived tokens. Same audit trail you already trust for humans.

Guardrails & policy enforcement

Tool-use whitelists, action approvals for high-impact operations, content/PII filters, and prompt-injection defenses at the gateway.

Audit & observability

Full conversation, tool, and reasoning logs piped into your SIEM. Anomaly detection on agent behavior. Replay any decision.

Production deployment

Anthropic Claude, OpenAI, Azure OpenAI, AWS Bedrock, or self-hosted. Containerized, observable, version-controlled. CI/CD pipelines for prompts and agent definitions.

Compliance mapping

Map your AI deployments to NIST AI RMF, EU AI Act, ISO 42001, and sector-specific frameworks. Audit-ready evidence by default.

24/7 monitoring

Our SOC watches agent traffic the way it watches network traffic. Drift detection, prompt-injection alerts, anomalous tool-use, and incident response within 15 minutes.

Why Paliton

An MSP and MSSP applying years of practice to a brand-new layer.

Generalist AI consultancies don't run production infrastructure. Generalist MSPs don't run a 24/7 SOC. Paliton does both — which is exactly what agentic AI requires.

The same SOC — now watching agents.

Our 24/7 SOC has watched endpoints, networks, and cloud workloads for years. Agent traffic is just another telemetry source feeding the same playbooks, the same on-call team, the same response SLA.

The same identity model — extended.

Zero Trust, conditional access, MFA enforcement, least-privilege scopes — we've designed and deployed these for hundreds of users across our customer base. Treating an agent as just another principal in that model is a small step, not a leap.

The same compliance team — new frameworks.

We already deliver SOC 2, HIPAA, CMMC, FedRAMP, and NIST 800-171 evidence for our customers. Adding NIST AI RMF, ISO 42001, and EU AI Act mappings is a new set of controls, not a new discipline.

The same network — designed for AI workloads.

Agents call APIs, hit databases, and stream tokens at scale. Our network engineers have already designed for high-throughput multi-site environments — agentic workloads simply join that traffic profile.

How we approach identity

Treat every agent like a non-human employee.

Securing agentic AI is less about traditional “app security” and more about giving every agent the same identity treatment a real employee gets — provisioning, scoping, auditing, offboarding. With fast-moving permissions.

The biggest mistake companies make is letting agents operate with broad API keys or shared credentials. Once you do, you lose visibility and control.

1

Real identity per agent — not just API keys.

  • Issue unique, traceable identities through your existing identity platform — Okta, Microsoft Entra ID, or Auth0
  • Map agents to roles, departments, and approval chains the same way you map humans
  • One agent compromised ≠ everything compromised
2

OAuth 2.0 / OIDC flows over static secrets.

  • Short-lived tokens (minutes, not months) — credential leak ≠ years of exposure
  • Refresh-token revocation is one click in your IdP, not a credential hunt across services
  • Every authentication logged through your existing audit pipeline
3

Identities at the agent or workflow level — not per app.

  • One identity per agent or workflow, never “the AI service account” with master access
  • Least-privilege scopes by default — generated, not negotiated
  • Agents are offboarded the way employees are: clean, complete, auditable
Where we ship

Where agents are already paying back.

Real deployments from our customers. Each one started with the question "can we trust this enough to put it in production?"

📞

Customer support automation

Tier-1 ticket triage, knowledge-base retrieval, and draft replies — with mandatory human approval before any reply ships. Cuts response time without putting wrong answers in front of customers.

🛡️

SOC analyst augmentation

Alert triage agents that summarize, correlate, and propose remediations — but only ever execute remediations a human approves. Time-to-triage drops 60%+.

📄

Document & contract intelligence

Extracting fields from contracts, RFPs, MSAs, NDAs. Flagging non-standard clauses for legal review. Searchable across the whole document corpus with semantic queries.

⚙️

IT operations automation

NOC agents that diagnose tickets, propose remediation steps, and (with approval) execute them via your existing RMM and ticketing tools. Reduces toil on repeatable issues.

60%
Avg reduction in alert
triage time
15 min
Incident response from
prompt-injection detection
100%
Decisions logged &
replayable
0
Hardcoded master
credentials in our deployments
FAQ

Common questions.

  • Anthropic Claude, OpenAI (GPT-4o, GPT-4 family), Azure OpenAI, AWS Bedrock (Claude, Titan, Llama), and self-hosted open models (Llama, Mistral) when sovereignty or sensitivity demands it. We pick what fits your data residency, compliance, and latency requirements — not by vendor incentive.
  • Default architecture: all model traffic via enterprise-tier endpoints (no training on your data, contractual data-handling guarantees). Sensitive workloads can run via Azure OpenAI in a dedicated tenant or self-hosted in your VPC. We never recommend free-tier or consumer model APIs for production.
  • Three layers: (1) Tool-use whitelists — the agent can only call functions you've explicitly permitted. (2) Action approvals — high-impact operations (anything moving money, sending external email, modifying production) require human sign-off. (3) Output validation — outputs are checked against schemas, citation requirements, and confidence thresholds before being released.
  • Five things, all enforced: (1) authenticated identity per agent, (2) least-privilege scopes, (3) full audit trail of inputs, reasoning, and outputs, (4) prompt-injection defenses at the gateway, (5) anomaly detection on behavior. If any of these are missing, you don't have a secure agent — you have an unaudited intern with the keys.
  • Either. Most engagements are co-build: your team owns the business logic and prompt design, we own the security envelope, deployment infrastructure, and ongoing monitoring. For teams without AI engineering capacity, we own end-to-end.
  • Discovery + design: 2–3 weeks. First agent in production with full observability: 4–6 weeks more. Each additional agent thereafter: 1–2 weeks since the security envelope is reusable. Compliance-heavy industries (healthcare, federal) can extend timelines for evidence collection.
  • Engagements typically start with a fixed-fee design + first-deployment package, then move to a managed monthly fee for ongoing security monitoring and platform operations. We share precise numbers after a 30-min discovery call — variables include number of agents, model tier, data sensitivity, and observability depth.

Ready to deploy agents you can trust?

30-minute architecture review. No commitment. We'll map where you are, what's safe to ship, and what needs hardening before it goes near production.

Book a Call