Seemplicity launches AI agents to speed up exposure remediation
Finding exposures is easy. Fixing them is where teams get stuck.
Seemplicity introduced four AI agents inside its Exposure Action Platform to clear that bottleneck: cut noise, route work to the right owners, and embed step-by-step fixes. Early adopters report up to 4x faster remediation and as much as a 95% improvement in resolving exposures.
The company, founded five years ago, fully leans into AI across its platform and raised $50 million in Series B funding in August.
The four agents and what they do
- Clarity Agent: Turns raw technical findings into short, narrative summaries so security and IT can review and communicate faster.
- Find the Fixer Agent: Cleans up scanner tags and maps findings to team structures to assign the right owner. Less ping-pong, fewer routing errors.
- Remediation Agent: Embeds actionable, step-by-step guidance (including commands and procedures) tailored to your tools and environment.
- Insights Agent: Converts dashboard data into prioritized intelligence you can act on and share with stakeholders.
As one product leader put it, "finding exposures isn't the problem, fixing them is." These agents focus on the messy, human side of remediation-ownership, context, and execution.
Why this matters to security leaders and MSSPs
Agent-based workflows are moving from pilots to production. Investors are flagging a shift: security for AI and AI for security now needs an agent-centric approach, including how these agents access systems and make decisions. That introduces upside and risk at the same time.
Practitioners are aligned on a key point: keep humans in the loop. "Comfort comes from boundaries and trust," said Certis Foster of Deepwatch. Teams are fine with agents triaging alerts, reporting, aggregating data, and analysis. Comfort drops when agents take autonomous actions-like containment or config changes-without explicit approval.
David Brumley of Carnegie Mellon and Mayhem Security added: agents can be major productivity boosters and cut time to remediation, but they can also hallucinate and act with confidence when they are wrong. Guardrails and a human-on-the-loop are non-negotiable.
Practical playbook to adopt AI agents safely
- Define trust boundaries: What agents can view, what they can suggest, and what requires human approval. Start with read-only and suggestions.
- Keep a human-on-the-loop: Require approval for containment, system modifications, and ticket closures until performance is proven.
- Start small: Use cases like alert triage, de-duplication, reporting, ownership mapping, and ticket enrichment.
- Get ownership right: Align scanner tags with your org chart and CMDB. Use the agent to auto-route tasks to accountable teams.
- Embed fixes at the source: Provide step-by-step remediation inside tickets with commands tailored to your stack.
- Instrument outcomes: Track MTTR, backlog burn-down, re-open rate, false-positive rate, and percent of findings with a named owner.
- Enforce guardrails: Least-privilege access, scoped API tokens, rate limits, dry-run modes, change windows, and a kill switch.
- Audit everything: Log prompts, actions, approvals, and results. Make it easy to review and learn.
- Stage before prod: Run agents in a sandbox or against synthetic data. Promote gradually with canaries.
- Close the comms gap: Use narrative summaries for executives and business owners. Technical steps for engineers. No translation layer needed.
- Vendor due diligence: Ask about data retention, model sources, update cadence, on-prem/virtual private deployment options, and compliance posture.
- Test failure modes: Red-team the agent. Introduce bad inputs and confirm it degrades safely, asks for help, or stops.
Questions to ask your team this week
- Which top three workflows would benefit from automated ownership and embedded fixes?
- What approvals are required before an agent can change anything in production?
- Do we have clear KPIs and a baseline to measure impact after 30/60/90 days?
- How are we logging and reviewing agent actions for audit and learning?
- What is our rollback plan if an agent makes a bad call?
Industry viewpoint
A leading venture firm recently noted that agent-based software operates with higher autonomy and broader system access than traditional apps. That raises two jobs for leadership: capture the efficiency gains and set strong controls so agents don't overstep. Both can be true at once.
Where to go from here
- If you're evaluating agentic security tooling, read investor perspectives on agent risks and opportunities for additional context. For example, see Menlo Ventures' thinking on AI agents here.
- Upskilling your managers on AI governance and practical workflows helps adoption stick. Explore role-based learning paths at Complete AI Training.
Bottom line for management
Seemplicity's agents go after the work that slows teams down: context, routing, and execution. If you pair them with clear guardrails, approvals, and sharp metrics, you'll cut time to fix without adding risk.
Start with well-bounded tasks, measure relentlessly, and expand only after you see consistent wins.
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