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CONTROLLED CHAOS
The Inevitable Marriage of
DevOps & Security
Kelly Shortridge (@swagitda_)
Dr. Nicole Forsgren (@nicolefv) Black Hat USA 2019
Hi, I’m Kelly
2
Hi, I’m Nicole
3
“Chaos isn’t a pit. Chaos is a ladder.”
― Petyr Baelish, Game of Thrones
4
Software is eating the world.
DevOps drives its devouring.
5
Infosec has a choice: marry DevOps
or be rendered impotent & irrelevant
6
Infosec isn’t invincible. Denial &
“DevSecOps” won’t save your future.
7
How should infosec control chaos &
make a marriage to DevOps last?
8
1. DevOps Dominion
2. The Metamorphosis
3. Time to D.I.E.
4. A Phoenix Rises
5. Marriage Vows
9
DevOps Dominion
DevOps is not automation or “agile”
11
DevOps is a mindset that unifies
responsibility and accountability.
12
DevOps has “crossed the chasm” –
the business benefits are too striking
13
DevOps integrates once-disparate
roles, encouraging “shifting left”
14
Infosec can join DevOps or watch as
DevOps carves its own secure path
15
Chaos & resilience is infosec’s future
16
Therefore, infosec & DevOps
priorities actually align…
17
What are DevOps’s priorities?
Optimization of software delivery
performance so tech delivers value
19
Stability & speed don’t conflict –
resilience & innovation are bffs
20
CI/CD: implement changes in prod
rapidly, sustainably, & safely
21
What metrics delineate “elite”
DevOps performers from the rest?
22
Lead time for changes: How long does
it take for committed code to
successfully run in production?
23
Release frequency: How often is code
deployed to production or released
to end users?
24
Time to Recovery (TTR):
How long does it take to restore
service?
25
Change failure rate: What percentage
of changes to production degrade
service & require remediation?
26
Elite High Medium Low
Lead time for
changes
< One day 1 day - 1 week
1 week –
1 month
1 month –
6 months
Release
frequency
On demand
(>1 daily)
1 per day –
1 per month
1 per week –
1 per month
1 per month –
1 per 6 months
Time to
recovery
< 1 hour < 1 day < 1 day
1 week –
1 month
Change failure
rate
0% – 15% 0% – 15% 0% – 15% 46% – 60%
27
The evidence: no tradeoff between
better infosec & DevOps leetness
28
Elites conduct security reviews &
implement changes in mere days
29
“DevOps doesn’t care about security”
is a lazy straw man. Stop it.
30
Security drives stronger DevOps
results. Now infosec must evolve.
31
The Metamorphosis
Partitioning of responsibility &
accountability engenders conflict
33
The real “DevSecOps”: DevOps will be
held accountable for security fixes
34
What goals should infosec pursue in
this evolution?
35
And… why should infosec goals
diverge from DevOps goals?
36
Infosec should support innovation in
the face of change – not add friction
37
Infosec has arguably failed, so “this is
how we’ve always done it” is invalid
38
Cloud & microservices created the
“Infosec Copernican Revolution”
39
But the data doesn’t lie: cloud & PaaS
contribute to elite performance
40
The Security of Chaos
“Things will fail” naturally extends
into “things will be pwned”
42
Security failure is when security
controls don’t operate as intended
43
What are the principles of chaotic
security engineering?
44
1. Expect that security controls will
fail & prepare accordingly
45
2. Don’t try to avoid incidents – hone
your ability to respond to them
46
What are the benefits of the chaos /
resilience approach?
47
Benefits: lowers remediation costs &
stress levels during real incidents
48
Benefits: minimizes end-user
disruption & improves confidence
49
Benefits: creates feedback loops to
foster understanding of systemic risk
50
The ability to automate “toil” away
should also appeal to infosec
51
Toil: manual, repetitive, tactical work
that doesn’t provide enduring value
52
Manual patching, provisioning 2FA /
ACLs, firewall rule management, etc.
53
What other ways can infosec become
more strategic?
54
Time to D.I.E.
C.I.A. triad: commonly used as a model
to balance infosec priorities
56
Confidentiality: withhold info from
people unauthorized to view it
57
Integrity: data is a trustworthy
representation of the original info
58
Availability: organization’s services
are available to end users
59
But these are security values, not
qualities that create security
60
We need a model promoting qualities
that make systems more secure
61
Instead, use the D.I.E. model:
Distributed, Immutable, Ephemeral
62
Distributed: multiple systems
supporting the same overarching goal
63
Distributed infrastructure reduces
risk of DoS attacks by design
64
Immutable: infrastructure that
doesn’t change after it’s deployed
65
Servers are now disposable “cattle”
rather than cherished “pets”
66
Immutable infra is more secure by
design – ban shell access entirely
67
Lack of control is scary, but unlimited
lives are better than nightmare mode
68
Ephemeral: infrastructure with a very
short lifespan (dies after a task)
69
Ephemerality creates uncertainty for
attackers (persistence = nightmare)
70
Installing a rootkit on a resource that
dies in minutes is a waste of effort
71
Optimizing for D.I.E. reduces risk by
design & supports resilience
72
A Phoenix Rises
What metrics support resilient
security engineering?
74
TTR is equally as important for
infosec as it is for DevOps
75
Time Between Failure (TBF) will lead
your infosec program astray
76
Extended downtime makes users sad,
not more frequent but trivial blips
77
Prioritizing failure inhibits innovation
78
Instead, harness failure as a tool to
help you prepare for the inevitable
79
TTR > TTD – who cares if you detect
quickly if you don’t fix it?
80
Game days: like planned firedrills
81
Prioritize game days based on
potential business impacts
82
Decision trees: start at target asset,
work back to easiest attacker paths
85
Determine the attacker’s least-cost
path (hint: it doesn’t involve 0day)
86
Architecting chaos
Begin with “dumb” testing before
moving to “fancy” testing
88
Controlling Chaos: Availability
89
Turning security events into
availability events appeals to DevOps
90
The existing repertoire of chaos eng
tools primarily covers availability
91
Chaos Monkey, Azure Fault Analysis
Service, Chaos-Lambda…
92
Kube-monkey, PowerfulSeal, Pod-
reaper, Pumba, Blockade…
93
Infosec teams can use these tools but
make attackers the source of failure
94
Controlling Chaos: Confidentiality
95
Microservices use multiple layers of
auth that preserve confidentiality
96
A service mesh is like an on-demand
VPN at the application level
97
Attackers are forced to escalate
privileges to access the iptables layer
98
Test: inject failure into your service
mesh to test authentication controls
99
Controlling Chaos: Integrity
100
Test: swap out certs in your ZTNs –
all transactions should fail
101
Test: modify encrypted data & see if
your FIM alerts on it
102
Test: retrograde libraries, containers,
other resources in CI/CD pipelines
103
D.I.E.ing is an art, like everything else
Controlling Chaos: Distributed
105
Distributed mostly overlaps with
availability in modern infra contexts
106
Multi-region services present a fun
opportunity to mess with attackers
107
Shuffle IP blocks regularly to change
attackers’ lateral movement game
108
Controlling Chaos: Immutable
109
Immutable infra is like a phoenix – it
disappears & comes back a lot
110
Volatile environments with continually
moving parts raise the cost of attack
111
Create rules like, “If there’s ever a
write to disk, crash the node”
112
Attackers must stay in-memory,
which hopefully makes them cry
113
Metasploit Meterpreter + webshell:
Touch passwords.txt & kaboom
114
Infosec teams can build Docker
images with a “bamboozle layer”
115
Mark garbage files as “unreadable” to
craft enticing bait for attackers
116
A potential goal: architect
immutability turtles all the way down
117
Test: inject attempts at writing to
disk to ensure detection & reversion
118
Treat changes to disk by adversaries
similarly to failing disks: mercy kill
119
Controlling Chaos: Ephemeral
120
Most infosec bugs are stated-related
– get rid of state, get rid of bugs
121
Reverse uptime: longer host uptime
adds greater security risk
122
Test: change API tokens & test if
services still accept old tokens
123
Test: inject hashes of old pieces of
data to ensure no data persistence
124
Use “arcade tokens” instead of using
direct references to data
125
Leverage lessons from toll fraud –
cloud billing becomes security signal
126
Test: exfil TBs or run a cryptominer
to inform billing spike detection
127
So, how should infosec work with
DevOps to implement all of this?
128
Marriage Vows
Infosec + DevOps = scalable love
How does this scalable love look?
Sit in on early design decisions &
demos – but say “No, and…” vs. “No.”
Provide input on tests so every
testing suite has infosec’s stamp on it
By the last “no” gate in the delivery
process, nearly all issues will be fixed
Infosec should focus on outcomes
that are aligned with business goals
135
TTR should become the preliminary
anchor of your security metrics
136
Security- & performance-related
gamedays can’t be separate species
137
Cultivate buy-in together for
resilience & chaos engineering
138
Visibility / observability: collecting
system information is essential
139
Your DevOps colleagues are likely
already collecting the data you need
140
Changing culture: change what
people do, not what they think
141
Conclusion
Security cannot force itself into
DevOps. It must marry it.
143
Chaos/resilience are natural homes
for infosec & represent its future.
144
Infosec must now evolve to unify
responsibility & accountability.
145
If not, infosec will sit at the kids’ table
until it is uninvited from the business.
146
Giving up control isn’t a harbinger of
doom. Resilience is a beacon of hope.
147
“You must have chaos within you to
give birth to a dancing star.”
― Friedrich Nietzsche
148
@swagitda_
/in/kellyshortridge
kelly@greywire.net
@nicolefv
/in/nicolefv
nicolefv@google.com
149

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